Fixed complexity tiny reservoir heterogeneous network for on-line ECG learning of anomalies
Abstract
The electrocardiogram (ECG), being one of the most extensively used signals to monitor cardiovascular diseases (CVDs), captures the heart’s arrhythmias. Patients with such pathology are often monitored for extended periods of time, requiring data storage, and a very time-consuming off-line search of anomalies. This is especially inefficient when indicative patterns in the biological signals are infrequent, requiring more analysis time of medical doctors, and entailing a difficult visual search task for the diagnosis.In this paper, we propose an automated deep learning pipeline based on reservoir computing (RC), followed by principal component analysis (PCA) and one-class support vector machine (OC-SVM), that can be used to perform on-line learning and real-time anomaly detection of pathological conditions and therefore to raise warnings. The learning step requires fixed computational complexity and memory that are affordable on a low-power microcontroller unit (MCU). During the learning phase, it uses a very limited amount of input data, e.g., 60 KB, which makes this work suitable for fast personalization when device restart is required. The detection accuracy has been evaluated on the publicly available MIT-BIH arrhythmia dataset, also modified to mimic temporal sliding of input tensors on the ECG streaming data. Best F1 score and accuracy are 91.5%, 95.4% with variance over the processed data of 0.001.
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Works before TinyML, such as [108], use CNN to classify audios captured through smartphones.
“Works already exist before adopting TinyML in the process, such as [108], where they use CNN to classify audios captured through smartphones.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [108]
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A Comprehensive Survey on TinyML
Abadade, Y., Temouden, A., Bamoumen, H., Benamar, N., Chtouki, Y., & Hafid, A. S. (2023) · IEEE Access · doi:10.1109/access.2023.3294111 · Cited by 366 · see citing works
- Authors
- Abdennadher, N., Pau, D., & Bruna, A.
- Affiliations
- University of Padua (IT)
- STMicroelectronics (Italy) (IT)
- Venue
- 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE)
- Publisher
- IEEE
- Published
- 2021-10-12
- Pages
- 233-237
- OpenAlex
- W4200560403
- Cited by
- 9
- References
- 19
- Citations by year
- 2025: 2 · 2024: 1 · 2023: 3 · 2022: 3
- FWCI
- 3.43
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Global Conference on Consumer Electronics (GCCE) (publishedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
- Keywords
- reservoir computing, ECG anomaly detection, arrhythmia detection, online learning, one-class support vector machine, principal component analysis, real-time anomaly detection, low-power microcontroller, MIT-BIH arrhythmia database, wearable health monitoring
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