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Cited by 91EEG and Brain-Computer InterfacesEpilepsy research and treatmentNon-Invasive Vital Sign Monitoring

Data quality evaluation in wearable monitoring

Open access
Böttcher, Sebastian, Vieluf, Solveig, Bruno, Elisa, Joseph, Boney, Epitashvili, Nino, Biondi, Andrea, Zabler, Nicolas, Glasstetter, Martin +7 more·2022·Scientific Reports
DOI 10.1038/s41598-022-25949-xPMC PMC9741649
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Abstract

Wearable recordings of neurophysiological signals captured from the wrist offer enormous potential for seizure monitoring. Yet, data quality remains one of the most challenging factors that impact data reliability. We suggest a combined data quality assessment tool for the evaluation of multimodal wearable data. We analyzed data from patients with epilepsy from four epilepsy centers. Patients wore wristbands recording accelerometry, electrodermal activity, blood volume pulse, and skin temperature. We calculated data completeness and assessed the time the device was worn (on-body), and modality-specific signal quality scores. We included 37,166 h from 632 patients in the inpatient and 90,776 h from 39 patients in the outpatient setting. All modalities were affected by artifacts. Data loss was higher when using data streaming (up to 49% among inpatient cohorts, averaged across respective recordings) as compared to onboard device recording and storage (up to 9%). On-body scores, estimating the percentage of time a device was worn on the body, were consistently high across cohorts (more than 80%). Signal quality of some modalities, based on established indices, was higher at night than during the day. A uniformly reported data quality and multimodal signal quality index is feasible, makes study results more comparable, and contributes to the development of devices and evaluation routines necessary for seizure monitoring.

AI snapshot

Written from the paper’s full text.

TL;DR

This paper proposes and applies a combined data-quality assessment for multimodal wrist-worn wearable recordings in epilepsy monitoring, covering data completeness, on-body time, and modality-specific signal quality. Across four epilepsy centers, all modalities were affected by artifacts, with streaming mode causing much more data loss than onboard recording and BVP showing the lowest signal quality.

Key findings

  • The study included 37,166 h from 632 inpatient patients and 90,776 h from 39 outpatient patients. Data loss was higher with streaming (up to 49% among inpatient cohorts, averaged across respective recordings) than with onboard device recording and storage (up to 9%).
  • On-body scores were consistently high across cohorts (more than 80%). Across all cohorts, TEMP had the highest signal quality (mean = 96.1%), followed by EDA (mean = 70.4%) and BVP (mean = 60.2%); BVP consistently had the lowest maximum quality per cohort.
  • BVP and EDA differed by time of day: BVP mean was 49.9% during daytime versus 70.7% at night, and EDA was 75.1% at night versus 65.6% during the day (both p < 0.001). Data completeness was also better at night (88.5%) than during the day (82.8%; p < 0.001), while on-body and TEMP scores did not differ by day/night (both < 1% mean difference).
  • In the BCH inpatient cohort, wrist versus ankle placement did not affect completeness or on-body scores (mean scores differed by only 0.1%), but wrist BVP and TEMP signal quality were lower by 4.7% (p < 0.001) and 2.1% (p = 0.015), respectively.

Methods

  • Prospective wearable data were collected from patients with epilepsy at four international epilepsy centers (BCH, KCL, MCR, UKF) in inpatient and outpatient settings; all participants wore an Empatica E4 wristband recording ACC, EDA, PPG/BVP, and TEMP.
  • Data quality was assessed with three components: data completeness (recorded vs. expected samples), on-body score (estimated percentage of time worn on the body), and modality-specific signal quality scores for EDA, BVP, and TEMP; ACC was not given a separate signal-quality score.
  • Signal-quality metrics used thresholding and rate-of-amplitude-change for EDA/TEMP and spectral entropy for BVP, scored per 1-min interval; statistical comparisons used two-tailed two-sample t-tests, with computations in MATLAB R2022a.

Limitations

  • The paper states that results must be interpreted in the setting of data collection: recruitment and enrollment procedures were not consistently aligned across centers, hardware and firmware versions may have differed, target recording length was undefined, and deployment regions differed.
  • The paper also states selection and information bias: pediatric inpatients were mostly drug-refractory, medication effects were not accounted for, outpatient environmental temperature was uncontrolled, day/night splitting did not reflect sleep times, and age differences were not analyzed in detail. It further notes that the BVP signal-quality measure has not been extensively validated for robust heart-rate estimation, and no accepted gold standard exists for EDA and TEMP beyond the methods used.
  • Evident from the text: outpatient cohorts were small (39 patients total), some cohorts targeted patients with high perceived compliance, and recording mode differed by center, which may confound center comparisons of data completeness.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1038/s41598-022-25949-x

Cites

Statistics

Nothing to recompute.

Claims the paper makes

0 of 95 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    Data loss in memory recording mode datasets was consistently below 10%.

    “While data loss (i.e., the inverse of data completeness) in the former datasets was consistently below 10%, the latter had up to 50% loss on average, with variances across individual recordings.”
    Quote in the paperSubstantial claim
  • Findingown data

    For one site, EDA signal quality was better in the inpatient setting than in the outpatient setting.

    “However, follow-up analysis per site revealed that for individual sites inpatient data showed higher data completeness values and for one site EDA signal quality was better than in the outpatient setting (see Supplement 1 for p-values).”
    Quote in the paperNot a substantial claim
  • Findingown data

    Visual analysis of E4 data recorded during a typical long-term period of daily living permits distinguishing intervals of wakefulness and sleep based on raw ACC and BVP data and BVP signal quality scores, because sleep has overall less movement and variability.

    “Visual analysis of E4 data recorded during a typical long-term period of daily living permits distinguishing intervals of wakefulness and sleep based on the raw ACC and BVP data and the BVP signal quality scores, based on overall less movement and variability during sleep.”
    Quote in the paperSubstantial claim
  • Findingown data

    Signal quality of some modalities, based on established indices, was higher at night than during the day.

    “Signal quality of some modalities, based on established indices, was higher at night than during the day.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The EDA signal falls to a zero value once the electrodes are not in contact with the skin anymore.
    • Rests on an assumption: The temperature values while the device is worn should fall within some reasonable values for skin or body temperature.
    • Rests on an assumption: The BVP signal is heavily influenced by external light and thus has a random and undefined range and shape during off-body segments, depending on factors of the incoming light like intensity or color.
  • Findingown data

    The variance of on-body scores among individuals was relatively low.

    “While some outliers in some cohorts exist, the variance among individuals was also relatively low, suggesting overall good compliance regarding wearing the device as uninterrupted as possible.”
    Quote in the paperNot a substantial claim
  • May combine two claims. 38
  • Classified as probably not a substantial claim. 43

  • May combine two claims.

    Findingown data

    The effect of artifacts on the signals ranges from no meaningful information being recorded when the wristband is not on the patient to differential and minor effects on data completeness and consistency.

    “The effect of artifacts on the signals ranges from no meaningful information being recorded when the wristband is not on the patient to differential and minor effects on data completeness and consistency.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    BVP and EDA indicate a difference between day- and nighttime scores (both p < 0.001).

    “BVP and EDA indicate a difference between day- and nighttime scores (both p < 0.001).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    On-body scores were consistently high across all cohorts, with average values above 80%.

    “On-body scores were consistently high across all cohorts, with average values above 80%.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    On-body scores and TEMP signal quality did not differ between day- and nighttime recordings (both < 1% mean difference).

    “The on-body scores and TEMP signal quality did not differ between day- and nighttime recordings (both < 1% mean difference).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Over all cohorts, the mean of BVP scores is 49.9% during the daytime, but 70.7% at night.

    “Over all cohorts, the mean of BVP scores is 49.9% during the daytime, but 70.7% at night.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Data loss in streaming mode datasets was up to 50% on average, with variances across individual recordings.

    “While data loss (i.e., the inverse of data completeness) in the former datasets was consistently below 10%, the latter had up to 50% loss on average, with variances across individual recordings.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Off-body time periods in E4 data are characterized by a temperature drop in TEMP, random noise or regular oscillations in BVP, and a low amplitude in ACC variance and EDA.

    “Furthermore, off-body time periods are characterized by a temperature drop in TEMP, random noise or regular oscillations in BVP, and a low amplitude in ACC variance and EDA (Fig. 3).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    For all cohorts, TEMP data had the highest signal quality (mean = 96.1%), followed by EDA (mean = 70.4%) and BVP (mean = 60.2%).

    “For all cohorts, the TEMP data had the highest signal quality (mean = 96.1%), followed by EDA (mean = 70.4%) and BVP (mean = 60.2%).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    In the BCH inpatient cohort, recording location did not affect data completeness and on-body scores; mean scores differed by only 0.1%.

    “Recording location did not affect the data completeness and on-body scores; in both scenarios, the mean scores differed by only 0.1%.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    For BVP and TEMP modalities, average signal quality scores at the wrist were lower by 4.7% (p < 0.001) and 2.1% (p = 0.015), respectively, compared with the ankle.

    “For the BVP and TEMP modalities, the average signal quality scores at the wrist were lower by 4.7% (p < 0.001) and 2.1% (p = 0.015), respectively.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    We included 37,166 h from 632 patients in the inpatient and 90,776 h from 39 patients in the outpatient setting.

    “We included 37,166 h from 632 patients in the inpatient and 90,776 h from 39 patients in the outpatient setting.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingnone given

    Artifact sources are multifaceted.

    “Artifact sources are multifaceted (Fig. 2).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Temperature measurements are least impacted by artifacts, followed by EDA and BVP recordings.

    “Overall, we found that temperature measurements are least impacted by artifacts, followed by EDA and BVP recordings.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingcitation

    The streaming mode allows for direct and uninterrupted control of the data, at the expense of data completeness, losing on average about half of the data in the cohorts presented here, likely due to Bluetooth range constraints.

    “Conversely, the streaming mode allows for direct and uninterrupted control of the data, at the expense of data completeness, losing on average about half of the data in the cohorts presented here (Table 4), likely due to Bluetooth range constraints20.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [20] Bruno, E. et al. Wearable devices for seizure detection: Practical experiences and recommendations from the wearables for epilepsy and research (WEAR) International Study Group. Epilepsia 62, 2307–2321 (2021). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Findingnone given

    Pediatric inpatients were mostly drug-refractory.

    “Pediatric inpatients were mostly drug-refractory”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Findingnone given

    Signal recording quality affects all autonomic modalities, and especially blood volume pulse recordings.

    “Signal recording quality affects all autonomic modalities, and especially blood volume pulse recordings.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Premisecitation

    Ambulatory seizure monitoring using wearables is now within reach.

    “Ambulatory seizure monitoring using wearables is now within reach1–7”
    Quote in the paperNot a substantial claim0 of 7 sources checked
    • cites [1] Onorati, F. et al. Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors. Epilepsia 58, 1870–1879 (2017). source

      Not checked in the free run.

    • cites [2] Beniczky, S., Karoly, P., Nurse, E., Ryvlin, P. & Cook, M. Machine learning and wearable devices of the future. Epilepsia 62, S116–S124 (2021). source

      Not checked in the free run.

    • cites [3] Hubbard, I., Beniczky, S. & Ryvlin, P. The challenging path to developing a mobile health device for epilepsy: The current landscape and where we go from here. Front. Neurol. 12, 740743 (2021). source

      Not checked in the free run.

    • cites [4] Freestone, D., Karoly, P. & Cook, M. A forward-looking review of seizure prediction. Curr. Opin. Neurol. 30, 1 (2017). source

      Not checked in the free run.

    • cites [5] Baud, M. & Schindler, K. Forecasting seizures: Not unthinkable anymore. Teach. Resour. Epileptol. 35, 156–161 (2018).

      Not checked in the free run.

    • cites [6] Böttcher, S. et al. Detecting tonic-clonic seizures in multimodal biosignal data from wearables: Methodology design and validation. JMIR MHealth UHealth 9, e27674 (2021). source

      Not checked in the free run.

    • cites [7] Böttcher, S. et al. Intra- and inter-subject perspectives on the detection of focal onset motor seizures in epilepsy patients. Sensors 22, 3318 (2022). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    Knowledge of artifacts and tools to assess data quality and generate data reliability ratings are crucial for subsequent analysis and consequently outcome reliability.

    “knowledge of artifacts and tools to assess data quality and generate data reliability ratings are crucial for subsequent analysis and consequently outcome reliability”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Established tools on how to determine the data quality of wearable signals as a basis for data selection are currently missing.

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given

    Having a baseline comparison between data sets of different sites and in different settings may considerably improve the interpretability of results in other studies using similar devices.

    “Having a baseline comparison between data sets of different sites and in different settings may considerably improve the interpretability of results in other studies using similar devices”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Established tools on how to determine the data quality of wearable signals as a basis for data selection are currently missing.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

    Premisecitation

    The quality of data can be expressed in many different ways, and data quality measures may also depend on the aims of the related analysis and project.

    “the quality of data can be expressed in many different ways, and data quality measures may also depend on the aims of the related analysis and project8–13”
    Quote in the paperNot a substantial claim0 of 6 sources checked
    • cites [8] Glasstetter, M. et al. Identification of ictal tachycardia in focal motor- and non-motor seizures by means of a wearable PPG sensor. Sensors 21, 6017 (2021). source

      Not checked in the free run.

    • cites [9] Nasseri, M. et al. Signal quality and patient experience with wearable devices for epilepsy management. Epilepsia 61, 16527 (2020). source

      Not checked in the free run.

    • cites [10] Bangaru, S. S., Wang, C. & Aghazadeh, F. Data quality and reliability assessment of wearable EMG and IMU sensor for construction activity recognition. Sensors 20, 5264 (2020). source

      Not checked in the free run.

    • cites [11] Pal, D., Vanijja, V., Arpnikanondt, C., Zhang, X. & Papasratorn, B. A quantitative approach for evaluating the quality of experience of smart-wearables from the quality of data and quality of information: An end user perspective. IEEE Access 7, 64266–64278 (2019). source

      Not checked in the free run.

    • cites [12] Kleckner, I. R. et al. Simple, transparent, and flexible automated quality assessment procedures for ambulatory electrodermal activity data. IEEE Trans. Biomed. Eng. 65, 1460–1467 (2018). source

      Not checked in the free run.

    • cites [13] Villeneuve, E., Harwin, W., Holderbaum, W., Sherratt, R. S. & White, R. Signal quality and compactness of a dual-accelerometer system for gyro-free human motion analysis. IEEE Sens. J. 16, 6261–6269 (2016). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Two components that contribute to measurable changes in EDA have been described: fast galvanic skin responses (phasic) act in the order of 0.5 to 5 s, and the tonic component acts in the order of minutes.

    “Two components that contribute to measurable changes in EDA have been described. The fast galvanic skin responses, also referred to as phasic responses, act in the order of 0.5 to 5 s, whereas the tonic component, which expresses in level changes, acts in the order of minutes26,27”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [26] Posada-Quintero, H. F. & Chon, K. H. Innovations in electrodermal activity data collection and signal processing: A systematic review. Sensors 20, 479 (2020). source

      Not checked in the free run.

    • cites [27] Critchley, H. D. & Harrison, N. A. Visceral influences on brain and behavior. Neuron 77, 624–638 (2013). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    A clean BVP signal from the Empatica E4 without noise from motion artifacts has a smooth quasi-periodic signature, which can be separated from random noise or motion artifacts by analyzing the entropy of the signal spectrum.

    “A clean BVP signal from the Empatica E4 without noise from motion artifacts has a smooth quasi-periodic signature, which can be separated from random noise or, more importantly, from motion artifacts by analyzing the entropy of the signal spectrum.”
    Quote in the paperSubstantial claim0 of 4 sources checked
    • cites [8] Glasstetter, M. et al. Identification of ictal tachycardia in focal motor- and non-motor seizures by means of a wearable PPG sensor. Sensors 21, 6017 (2021). source

      Not checked in the free run.

    • cites [9] Nasseri, M. et al. Signal quality and patient experience with wearable devices for epilepsy management. Epilepsia 61, 16527 (2020). source

      Not checked in the free run.

    • cites [23] Nasseri, M. et al. Non-invasive wearable seizure detection using long–short-term memory networks with transfer learning. J. Neural Eng. 18, 056017 (2021). source

      Not checked in the free run.

    • cites [33] Roy, M. S., Gupta, R. & Sharma, K. D. Photoplethysmogram signal quality evaluation by unsupervised learning approach. In 2020 IEEE Applied Signal Processing Conference (ASPCON), 6–10. https://doi.org/10.1109/ASPCON49795.2020.9276733 (IEEE, 2020). source

      Not checked in the free run.

  • May combine two claims.

    Study design and conductown data

    In the ambulatory datasets, several participants dropped out before completing the planned 6-month follow-up (KCL N = 5/15; UKF N = 1/12), and for some patients the follow-up period was deliberately shortened to 3 months (KCL N = 3/15; UKF N = 1/12); these data were included in the quality assessment.

    “In the ambulatory datasets from the King’s College London (KCL) and Medical Center University of Freiburg (Universitätsklinikum Freiburg, UKF) sites, several participants dropped out of the study before they completed the planned 6-month follow-up (KCL N = 5/15; UKF N = 1/12). Furthermore, for some patients, the follow-up period was deliberately shortened to 3 months (KCL N = 3/15; UKF N = 1/12).”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The data from dropped-out participants and those with deliberately shortened follow-up can be included in the quality assessment without biasing the results.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The shape of a clean PPG signal is closely related to the blood pulse wave of the human body.

    “The shape of a clean PPG signal is closely related to the blood pulse wave of the human body25”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [25] Elgendi, M. On the analysis of fingertip photoplethysmogram signals. Curr. Cardiol. Rev. 8, 14–25 (2012). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    As the sensor directly depends on reflectance properties of the skin tissue, different skin colors may change the overall signal quality of the recorded PPG data.

    “As the sensor directly depends on reflectance properties of the skin tissue, different skin colors may change the overall signal quality of the recorded PPG data49,50.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [49] Tasli, H. E., Gudi, A. & den Uyl, M. Remote PPG based vital sign measurement using adaptive facial regions. In 2014 IEEE International Conference on Image Processing (ICIP), 1410–1414. https://doi.org/10.1109/ICIP.2014.7025282 (IEEE, 2014). source

      Not checked in the free run.

    • cites [50] Shcherbina, A. et al. Accuracy in wrist-worn, sensor-based measurements of heart rate and energy expenditure in a diverse cohort. J. Pers. Med. 7, 3 (2017). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Ambient artifacts include light artifacts interfering with the light of the PPG sensor, weather conditions such as high humidity which increases the moisture level of the skin surface and thus its conductivity, and coverage of the device for example by a blanket which increases the temperature and thereby potentially EDA levels.

    “Ambient artifacts are for example light artifacts interfering with the light of the PPG sensor43, weather conditions, such as high humidity which increases the moisture level of the skin surface and thus its conductivity44, as well as coverage of the device for example by a blanket which increases the temperature and thereby potentially EDA levels45.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [43] Lee, J. et al. Comparison between red, green and blue light reflection photoplethysmography for heart rate monitoring during motion. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 1724–1727. https://doi.org/10.1109/EMBC.2013.6609852 (IEEE, 2013). source

      Not checked in the free run.

    • cites [44] Boucsein, W. Electrodermal Activity (Springer, 2012). source

      Not checked in the free run.

    • cites [45] Sano, A., Picard, R. W. & Stickgold, R. Quantitative analysis of wrist electrodermal activity during sleep. Int. J. Psychophysiol. 94, 382–389 (2014). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    A complete loss of contact results in a 0-line for the EDA signal; motion artifacts without contact loss result in abrupt level changes or high amplitude fluctuations; and PPG shows considerable deviation from expected signal patterns.

    “While a complete loss of contact results in a 0-line for the EDA signal, motion artifacts without contact loss result in abrupt level changes or high amplitude fluctuations, and PPG shows considerable deviation from expected signal patterns24,42.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [24] McCarthy, C., Pradhan, N., Redpath, C. & Adler, A. Validation of the empatica E4 wristband. In 2016 IEEE EMBS International Student Conference (ISC), 1–4. https://doi.org/10.1109/EMBSISC.2016.7508621 (IEEE, 2016). source

      Not checked in the free run.

    • cites [42] Milstein, N. & Gordon, I. Validating measures of electrodermal activity and heart rate variability derived from the empatica E4 utilized in research settings that involve interactive dyadic states. Front. Behav. Neurosci. 14, 148 (2020). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    The recording location (wrist or ankle) and the time of day can impact the signal quality for all modalities, especially for BVP and EDA.

    “We found that the recording location, here wrist or ankle, and the time of day can impact the signal quality for all modalities, especially for BVP and EDA.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Device-specific artifacts are often difficult to detect and typically include electrical noise, indirect changes in the signal due to a rise in the operating temperature of the device over time, time shifts, and calibration offsets.

    “Device-specific artifacts are often difficult to detect and typically include electrical noise, indirect changes in the signal due to a rise in the operating temperature of the device over time, time shifts, and calibration offsets20.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [20] Bruno, E. et al. Wearable devices for seizure detection: Practical experiences and recommendations from the wearables for epilepsy and research (WEAR) International Study Group. Epilepsia 62, 2307–2321 (2021). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The Empatica E4 device has been reported in one study to perform worse than some consumer-grade wearable devices.

    “In this context, the Empatica E4 device has been reported in one study to perform worse than some consumer-grade wearable devices14.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [14] Bent, B., Goldstein, B. A., Kibbe, W. A. & Dunn, J. P. Investigating sources of inaccuracy in wearable optical heart rate sensors. NPJ Dig. Med. 3, 1–9 (2020). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Artifacts originate from the environment, the person wearing the wearable, and the device itself.

    “Artifacts originate from the environment, the person wearing the wearable, and the device itself41.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [41] Islam, Md. K., Rastegarnia, A. & Sanei, S. Signal artifacts and techniques for artifacts and noise removal. In Signal Processing Techniques for Computational Health Informatics Vol. 192 (eds Ahad, M. A. R. & Ahmed, M. U.) 23–79 (Springer, 2021). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The skin color of the subject has been a major point of discussion concerning the quality of PPG recordings.

    “Prominently, the skin color of the subject has been a major point of discussion concerning the quality of PPG recordings14,47,48.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [14] Bent, B., Goldstein, B. A., Kibbe, W. A. & Dunn, J. P. Investigating sources of inaccuracy in wearable optical heart rate sensors. NPJ Dig. Med. 3, 1–9 (2020). source

      Not checked in the free run.

    • cites [47] Colvonen, P. J. Response to: Investigating sources of inaccuracy in wearable optical heart rate sensors. NPJ Digit. Med. 4, 38 (2021). source

      Not checked in the free run.

    • cites [48] Bent, B., Enache, O. M., Goldstein, B., Kibbe, W. & Dunn, J. P. Reply: Matters arising ‘investigating sources of inaccuracy in wearable optical heart rate sensors’. Npj Digit. Med. 4, 39 (2021). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Signal quality assessments have been done under more general circumstances, but wearable data quality in the context of epilepsy has not been a widely researched topic so far.

    “Signal quality assessments have been done under more general circumstances10,34–37, but wearable data quality in the context of epilepsy has not been a widely researched topic so far8,9,38.”
    Quote in the paperSubstantial claim0 of 8 sources checked
    • cites [10] Bangaru, S. S., Wang, C. & Aghazadeh, F. Data quality and reliability assessment of wearable EMG and IMU sensor for construction activity recognition. Sensors 20, 5264 (2020). source

      Not checked in the free run.

    • cites [34] Cho, S., Weng, C., Kahn, M. G. & Natarajan, K. Identifying data quality dimensions for person-generated wearable device data: Multi-method study. JMIR MHealth UHealth 9, e31618 (2021). source

      Not checked in the free run.

    • cites [35] Pradhan, N., Rajan, S. & Adler, A. Evaluation of the signal quality of wrist-based photoplethysmography. Physiol. Meas. 40, 065008 (2019). source

      Not checked in the free run.

    • cites [36] Bizzego, A., Gabrieli, G., Furlanello, C. & Esposito, G. Comparison of wearable and clinical devices for acquisition of peripheral nervous system signals. Sensors 20, 6778 (2020). source

      Not checked in the free run.

    • cites [37] Holt, S. G. et al. Monitoring skin temperature at the wrist in hospitalised patients may assist in the detection of infection. Intern. Med. J. 50, 685–690 (2020). source

      Not checked in the free run.

    • cites [8] Glasstetter, M. et al. Identification of ictal tachycardia in focal motor- and non-motor seizures by means of a wearable PPG sensor. Sensors 21, 6017 (2021). source

      Not checked in the free run.

    • cites [9] Nasseri, M. et al. Signal quality and patient experience with wearable devices for epilepsy management. Epilepsia 61, 16527 (2020). source

      Not checked in the free run.

    • cites [38] Bruno, E., Biondi, A., Thorpe, S. & Richardson, M. P. Patients self-mastery of wearable devices for seizure detection: A direct user-experience. Seizure 81, 236–240 (2020). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    Moving to an ambulatory setting may further amplify the impact on data loss.

    “Moving to an ambulatory setting may further amplify the impact on data loss.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisenone given

    The Empatica E4 device has been used in a number of other studies related to epilepsy monitoring as well as in other contexts.

    “The Empatica E4 device, specifically, has been used in a number of other studies related to epilepsy monitoring as well as in other contexts.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Premisecitation

    In seizure monitoring, well-established wearables are the Empatica Embrace and E4.

    “In seizure monitoring, well-established wearables are the Empatica Embrace and E466.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [66] Regalia, G., Onorati, F., Lai, M., Caborni, C. & Picard, R. W. Multimodal wrist-worn devices for seizure detection and advancing research: Focus on the Empatica wristbands. Epilepsy Res. 153, 79–82 (2019). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The Empatica E4 may be unsuited for monitoring everyday activities.

    “The device has also been evaluated in general purpose real-world settings, with results indicating that the Empatica E4 may be unsuited for monitoring everyday activities70,71.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [70] Vila, G., Godin, C., Charbonnier, S. & Campagne, A. Real-time quality index to control data loss in real-life cardiac monitoring applications. Sensors 21, 5357 (2021). source

      Not checked in the free run.

    • cites [71] Barrios, L., Oldrati, P., Santini, S. & Lutterotti, A. Evaluating the accuracy of heart rate sensors based on photoplethysmography for in-the-wild analysis. In Proc. 13th EAI International Conference on Pervasive Computing Technologies for Healthcare, 251–261. https://doi.org/10.1145/3329189.3329215 (ACM, 2019). source

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Study participants had the best compliance concerning wearing the device and correctly fitting it, while compliance was worst when it came to pairing the device to a Bluetooth companion device, with frequent connection issues.

    “Study participants had the best compliance concerning wearing the device and correctly fitting it, while compliance was worst when it came to pairing the device to a Bluetooth companion device, with frequent connection issues.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [38] Bruno, E., Biondi, A., Thorpe, S. & Richardson, M. P. Patients self-mastery of wearable devices for seizure detection: A direct user-experience. Seizure 81, 236–240 (2020). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    There is high variance in PPG signal quality across different devices, such that a device-specific consideration seems appropriate.

    “Naturally, the signal quality of PPG data in general is often discussed in other work as it is highly susceptible to motion artifacts57, however, there is also a high variance in PPG signal quality across different devices67, such that a device-specific consideration seems appropriate.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [57] Charlton, P. H. et al. Wearable photoplethysmography for cardiovascular monitoring. Proc. IEEE 110, 355–381 (2022). source

      Not checked in the free run.

    • cites [67] Wolling, F. & Van Laerhoven, K. The quest for raw signals: A quality review of publicly available photoplethysmography datasets. In Proc. Third Workshop on Data: Acquisition to Analysis, 14–19. https://doi.org/10.1145/3419016.3431485 (ACM, 2020). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductcitation

    Our signal quality metric is building on, expanding, and validating this research group's average values for what they describe as good, marginal, and noise data.

    “Still, our signal quality metric is building on, expanding, and validating this research group’s average values for what they describe as good, marginal, and noise data.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [9] Nasseri, M. et al. Signal quality and patient experience with wearable devices for epilepsy management. Epilepsia 61, 16527 (2020). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    The recording mode of the wearable device was the main cause of differences in data loss, with the two centers using streaming mode showing considerably less overall data completeness.

    “The recording mode of the wearable device was the main cause of differences in data loss, with the two centers using the streaming mode showing considerably less overall data completeness (Table 4).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    In another study assessing PPG as a measure for epileptic seizures, the data from three out of eleven patients wearing an Empatica E4 device was not usable, since no seizure periods without motion artifacts could be recorded.

    “In another study assessing PPG as a measure for epileptic seizures, the data from three out of eleven patients wearing an Empatica E4 device was not usable, since no seizure periods without motion artifacts could be recorded69.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [69] El Atrache, R. et al. Photoplethysmography: A measure for the function of the autonomic nervous system in focal impaired awareness seizures. Epilepsia 61, 1617–1626 (2020). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Wearables record thermal activity by measuring the skin temperature at the recording location; this is often lower than commonly known values for human core body temperature, e.g., when determining fever, and is more prone to environmental influences.

    “Wearables record thermal activity by measuring the skin temperature at the recording location. This is often lower than commonly known values for human core body temperature, e.g., when determining fever, and is more prone to environmental influences60.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [60] Huan, J. et al. A wearable skin temperature monitoring system for early detection of infections. IEEE Sens. J. 22, 1670–1679 (2022). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation

    These results directly coincide with our assessment of the data in terms of good on-body scores and bad data completeness.

    “These results directly coincide with our assessment of the data in terms of good on-body scores and bad data completeness.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    BVP data is dependent on measuring reflected light from a light-emitting diode (LED), and this signal can be heavily skewed by contamination from external light sources and motion artifacts, sometimes to the point of being irrecoverable through retrospective data processing.

    “BVP data is dependent on measuring reflected light from a light-emitting diode (LED), and this signal can be heavily skewed by contamination from external light sources and motion artifacts, sometimes to the point of being irrecoverable through retrospective data processing40,57,58.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [40] Ismail, S., Akram, U. & Siddiqi, I. Heart rate tracking in photoplethysmography signals affected by motion artifacts: A review. EURASIP J. Adv. Signal Process. 2021, 5 (2021). source

      Not checked in the free run.

    • cites [57] Charlton, P. H. et al. Wearable photoplethysmography for cardiovascular monitoring. Proc. IEEE 110, 355–381 (2022). source

      Not checked in the free run.

    • cites [58] Castaneda, D., Esparza, A., Ghamari, M., Soltanpur, C. & Nazeran, H. A review on wearable photoplethysmography sensors and their potential future applications in health care. Int. J. Biosens. Bioelectron. 4, 195–202 (2018).

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The device memory mode is very reliable and data loss only occurs whenever the device is switched off to swap it or when using the shower.

    “The device memory mode is very reliable and data loss only occurs whenever the device is switched off to swap it or when using the shower, as seen in previous studies20,21,54.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [20] Bruno, E. et al. Wearable devices for seizure detection: Practical experiences and recommendations from the wearables for epilepsy and research (WEAR) International Study Group. Epilepsia 62, 2307–2321 (2021). source

      Not checked in the free run.

    • cites [21] Tang, J. et al. Seizure detection using wearable sensors and machine learning: Setting a benchmark. Epilepsia 62, 1807–1819 (2021). source

      Not checked in the free run.

    • cites [54] De Brouwer, M. et al. mBrain: Towards the continuous follow-up and headache classification of primary headache disorder patients. BMC Med. Inform. Decis. Mak. 22, 87 (2022). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    In our studies across centers utilizing the device, the battery life tends to worsen with prolonged and regular use in the range of only a few months.

    “Furthermore, in our studies across centers utilizing the device, the battery life tends to worsen with prolonged and regular use55 in the range of only a few months20.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [55] Han, X. et al. A review on the key issues of the lithium ion battery degradation among the whole life cycle. eTransportation 1, 100005 (2019). source

      Not checked in the free run.

    • cites [20] Bruno, E. et al. Wearable devices for seizure detection: Practical experiences and recommendations from the wearables for epilepsy and research (WEAR) International Study Group. Epilepsia 62, 2307–2321 (2021). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    These conditions have a direct impact on BVP data recorded over multiple days, which is reflected by the overall worst signal quality results in our cohorts for this modality.

    “These conditions have a direct impact on BVP data recorded over multiple days, which is reflected by the overall worst signal quality results in our cohorts for this modality.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    One study reports that more than half of epileptic seizures in a data set could not be detected from Empatica E4 PPG data due to motion artifacts.

    “One study, for example, reports that more than half of epileptic seizures in a data set could not be detected from Empatica E4 PPG data due to motion artifacts68.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [68] Vandecasteele, K. et al. Automated epileptic seizure detection based on wearable ECG and PPG in a hospital environment. Sensors 17, 2338 (2017). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    The design and functionality of ACC sensors may lead to sensor noise and other inconsistencies like sensor saturation or displacement.

    “The design and functionality of ACC sensors may lead to sensor noise and other inconsistencies like sensor saturation or displacement63–65.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [63] Jao, C.-S. & Shkel, A. M. A reconstruction filter for saturated accelerometer signals due to insufficient FSR in foot-mounted inertial navigation system. IEEE Sens. J. 22, 695–706 (2022). source

      Not checked in the free run.

    • cites [64] Mohd-Yasin, F., Korman, C. E. & Nagel, D. J. Measurement of noise characteristics of MEMS accelerometers. Solid-State Electron. 47, 357–360 (2003). source

      Not checked in the free run.

    • cites [65] Kunze, K. & Lukowicz, P. Dealing with sensor displacement in motion-based onbody activity recognition systems. In Proc. 10th International Conference on Ubiquitous Computing—UbiComp’08, 20. https://doi.org/10.1145/1409635.1409639 (ACM Press, 2008). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Artifacts in the EDA signal are often caused by connection loss of the electrodes to the skin due to body motion, and present as a sharp decrease or increase, or fall to a zero-line value altogether.

    “Artifacts in the EDA signal are thus often caused by connection loss of the electrodes to the skin due to, e.g., body motion, and present as a sharp decrease or increase, or fall to a zero-line value altogether12.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [12] Kleckner, I. R. et al. Simple, transparent, and flexible automated quality assessment procedures for ambulatory electrodermal activity data. IEEE Trans. Biomed. Eng. 65, 1460–1467 (2018). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisenone given

    The causes, effects, and interactions of thermoregulation in the context of epilepsy are not well understood.

    “While thermoregulation in the context of epilepsy has been a topic of some research in the past61,62, its causes, effects, and interactions are not well understood.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [61] Pollandt, S. & Bleck, T. P. Thermoregulation in epilepsy. In Handbook of Clinical Neurology Vol. 157 (ed. Romanovsky, A. A.) 737–747 (Elsevier, 2018).

      Not checked in the free run.

    • cites [62] Stefan, H., Feichtinger, M. & Black, A. Autonomic phenomena of temperature regulation in temporal lobe epilepsy. Epilepsy Behav. 4, 65–69 (2003). source

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Premisenone given

    The E4 is a research device that allows accessing raw data and, in addition to the sensors integrated into the Embrace device, records cardiac activity.

    “The E4 is a research device that allows accessing raw data and, in addition to the sensors integrated into the Embrace device, records cardiac activity.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    The impact of the drug-refractory pediatric inpatient selection on data quality is presumably small.

    “however, the impact on data quality is presumably small.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationreasoning

    The differences in EDA and BVP signal quality may be related to reduced movement during sleep, as well as usually darker environments specifically concerning the BVP sensor.

    “The differences in EDA and BVP signal quality may be related to reduced movement during sleep, as well as usually darker environments specifically concerning the BVP sensor.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Study design and conductreasoning

    The results need to be interpreted in the setting of minor variations in the data acquisition setups.

    “the results also need to be interpreted in the setting of minor variations in the data acquisition setups.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductnone given

    The outpatient datasets all have comparably small cohort sizes such that the results may be skewed.

    “However, the outpatient datasets all have comparably small cohort sizes such that the results may be skewed.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation

    Deployment in different regions may have contributed to variability.

    “Additionally, beyond differences in demographics and device placements, deployment in different regions may have contributed to variability78.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [78] Profita, H. P. et al. Don’t mind me touching my wrist: A case study of interacting with on-body technology in public. In Proc. 17th Annual International Symposium on International Symposium on Wearable Computers—ISWC ’13, 89. https://doi.org/10.1145/2493988.2494331 (ACM Press, 2013). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The authors did not analyze in detail any differences in data and signal quality with respect to age, particularly concerning very young patients recruited in the BCH cohort.

    “In addition, we did not analyze in detail any differences in data and signal quality with respect to age, particularly concerning very young patients recruited in the BCH cohort.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    The lack of difference between ambulatory and inpatient signal quality may be explained by the smaller and more selective ambulatory cohorts.

    “This may be explained by the smaller and more selective ambulatory cohorts.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The target recording length was undefined in all cohorts.

    “Furthermore, besides a rough range of up to 14 days for inpatient recordings and up to 12 months for outpatient recordings, the target recording length was undefined in all cohorts.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    The better BVP and TEMP quality at the ankle could be explained by relatively fewer movements of the ankle location as opposed to the wrist, especially in the inpatient setting.

    “This could be explained by the relatively fewer movements of the ankle location as opposed to the wrist, especially in the inpatient setting.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    Higher data completeness during the night likely results from fewer risks for the wearable device to be disconnected, such as moving away from the recording device in the outpatient setting, or otherwise stopping recording accidentally.

    “Higher data completeness during the night likely results from fewer risks for the wearable device to be disconnected, such as moving away from the recording device in the outpatient setting, or otherwise stopping recording accidentally.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The selection process for some cohorts targeted patients with a high perceived potential for compliance, based on familiarity with wearable technology and willingness to participate in months-long studies.

    “The selection process for some cohorts targeted patients with a high perceived potential for compliance, based on familiarity with wearable technology and willingness to participate in months-long studies.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    Robust wearable monitoring systems would substantially improve both seizure reporting as well as alarms during the nighttime periods in patients' daily living.

    “As such, robust wearable monitoring systems would substantially improve both seizure reporting as well as alarms during the nighttime periods in patients’ daily living.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    The smaller cohorts and more targeted recruitment process could have introduced some selection bias with regard to compliance.

    “although the smaller cohorts and more targeted recruitment process could have introduced some selection bias with regard to compliance.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    Lower EDA quality at the ankle is likely due to sweat gland distributions and exact sensor placement on the ankle.

    “Lower EDA quality at the ankle is likely due to sweat gland distributions and exact sensor placement on the ankle, i.e., medial or lateral.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    Enrollment procedures for the ambulatory studies partially included new patients who did not necessarily participate in an inpatient study.

    “Enrollment procedures for the ambulatory studies also partially included new patients who did not necessarily participate in an inpatient study.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    Different hardware and firmware versions may have been used across and even within cohorts.

    “While all recordings were performed with an Empatica E4 device, different hardware and firmware versions may have been used across and even within cohorts.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The Empatica E4 has been shown to facilitate seizure detection by heart rate estimation from PPG despite its susceptibility to motion artifacts.

    “Moreover, the E4 has been shown to facilitate seizure detection by heart rate estimation from PPG despite its susceptibility to motion artifacts8,72.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [8] Glasstetter, M. et al. Identification of ictal tachycardia in focal motor- and non-motor seizures by means of a wearable PPG sensor. Sensors 21, 6017 (2021). source

      Not checked in the free run.

    • cites [72] van Andel, J., Ungureanu, C., Aarts, R., Leijten, F. & Arends, J. Using photoplethysmography in heart rate monitoring of patients with epilepsy. Epilepsy Behav. 45, 142–145 (2015). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    Some signal quality metrics employed in this study still lack a structured and comprehensive validation on ground truth data.

    “While all of the signal quality metrics employed in this study have been used in other research before, some still lack a structured and comprehensive validation on ground truth data, which is a necessary next step.”
    Quote in the paperNot a substantial claim
71 of 71

  • The paper relies on this without support.

    Established tools on how to determine the data quality of wearable signals as a basis for data selection are currently missing.

    “established tools on how to determine the data quality of wearable signals as a basis for data selection are currently missing”

  • The paper relies on this without support.

    The quality of the raw recorded data from wearables is a frequently underreported aspect in clinical studies employing these wearables, especially in quantitative terms.

    “The quality of the raw recorded data from wearables is a frequently underreported aspect in clinical studies employing these wearables, especially in quantitative terms.”

  • The paper relies on this without support.

    Data quality remains one of the most challenging factors that impact data reliability.

    “data quality remains one of the most challenging factors that impact data reliability”

  • The paper relies on this without support.

    The EDA signal falls to a zero value once the electrodes are not in contact with the skin anymore.

    “The EDA signal falls to a zero value once the electrodes are not in contact with the skin anymore.”

  • The paper relies on this without support.

    The temperature values while the device is worn should fall within some reasonable values for skin or body temperature.

    “The temperature values while the device is worn should fall within some reasonable values for skin or body temperature.”

  • The paper relies on this without support.

    The BVP signal is heavily influenced by external light and thus has a random and undefined range and shape during off-body segments, depending on factors of the incoming light like intensity or color.

    “The BVP modality was not used in the on-body assessment because its signal is heavily influenced by external light and thus has a random and undefined range and shape during off-body segments, depending on factors of the incoming light like intensity or color.”

  • The paper relies on this without support.

    Continuous data collection in outpatient, real-life situations and data analysis in real-time bear multiple challenges before this can be effectively implemented.

    “continuous data collection in outpatient, real-life situations and data analysis in real-time bear multiple challenges before this can be effectively implemented”

  • The paper relies on this without support.

    The Empatica E4 is the only certified research-grade epilepsy monitoring device on the market that provides raw data for all of the modalities investigated here, relevant to epileptic seizure detection.

    “Even so, it remains the only certified research-grade epilepsy monitoring device on the market that provides raw data for all of the modalities investigated here, relevant to epileptic seizure detection.”

  • The paper relies on this without support.

    Data recorded during off-body periods contains no relevant information.

    “Since the data from such periods contains no relevant information, data may need to be edited accordingly before performing the signal quality checks suggested here.”

  • The paper relies on this without support.

    The data from dropped-out participants and those with deliberately shortened follow-up can be included in the quality assessment without biasing the results.

    “These data were included in this quality assessment.”

  • The authors state this as an assumption.

    Motion artifacts occur more frequently in the outpatient setting.

    “suggesting that the presumably more frequently occurring motion artifacts in the outpatient setting did not have a significant effect on signal quality.”

  • The paper relies on this without support.

    Patients or caregivers choose the ankle location based on comfort and where other medical devices are placed.

    “Patients or caregivers choose the ankle location based on comfort and where other medical devices are placed.”

  • The paper relies on this without support.

    Some anti-seizure medications might influence autonomic activities.

    “while some anti-seizure medications might influence autonomic activities.”

  • The paper relies on this without support.

    Environmental temperature impacts TEMP and EDA measures.

    “we could not control for environmental temperature that impacts TEMP and EDA measures.”
14 of 14

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References 80 · Cited by 91

Research works printed but not linked: 2

  1. Baud, M. & Schindler, K. Forecasting seizures: Not unthinkable anymore. Teach. Resour. Epileptol. 35, 156–161 (2018).
  2. Braithwaite, J. J., Watson, D. G., Jones, R. & Rowe, M. A guide for analysing electrodermal activity (EDA) & skin conductance responses (SCRs) for psychological experiments. Psychophysiology 49, 1017–1034 (2013).

Authors
Böttcher, S., Vieluf, S., Bruno, E., Joseph, B., Epitashvili, N., Biondi, A., Zabler, N., Glasstetter, M., Dümpelmann, M., Van Laerhoven, K., Nasseri, M., Brinkman, B. H., Richardson, M. P., Schulze-Bonhage, A., & Loddenkemper, T.
Affiliations
  • University of Freiburg (DE)
  • Boston Children's Hospital (US)
  • King's College London (GB)
  • Mayo Clinic (US)
  • University of North Florida (US)
Corresponding author
Böttcher, Sebastian
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2022-12-10
Volume
12
Issue
1
Pages
21412-21412
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-022-25949-x
PMC
PMC9741649
OpenAlex
W4311666187
Cited by
91
References
80
Citations by year
2026: 30 · 2025: 23 · 2024: 18 · 2023: 20
FWCI
7.86
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
Funders
  • Innovative Medicines Initiative (115902)
  • Deutsche Forschungsgemeinschaft (VI 1088/1-1)
  • Epilepsy Foundation (My Seizure Gauge)
  • Mayo Clinic
  • Epilepsy Research UK
  • National Institutes of Health (UG3 NS123066)
  • National Science Foundation (CBET-2138378)
  • Epilepsy Research Fund
  • Universitätsklinikum Freiburg
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Health Sciences › Medicine › Psychiatry and Mental health › Epilepsy research and treatment
  • Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
Keywords
wearable monitoring, data quality assessment, seizure monitoring, epilepsy, wrist-worn sensors, accelerometry, electrodermal activity, blood volume pulse, skin temperature, signal artifacts, data loss, stream processing
MeSH terms
Data Accuracy, Wearable Electronic Devices, Epilepsy, Humans, Seizures, Reproducibility of Results

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.

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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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