SpikeBASE: Spiking Neural Learning Algorithm With Backward Adaptation of Synaptic Efflux

Stauffer, Jake; Zhang, Qingxue

doi:10.1109/tc.2022.3197089

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…It will be promising to further enhance the system with more effective pattern extraction [ 37 , 38 , 39 , 40 ] studies. The proposed approaches could also be generalized to other applications or signals [ 41 , 42 , 43 , 44 ] for event detection, template signal learning, and signal quality purification.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Electrocardiogram for Mobile Cardiac Pattern Extraction

Zhang

Zhou

2023

Sensors

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Background: Internet-of-things technologies are reshaping healthcare applications. We take a special interest in long-term, out-of-clinic, electrocardiogram (ECG)-based heart health management and propose a machine learning framework to extract crucial patterns from noisy mobile ECG signals. Methods: A three-stage hybrid machine learning framework is proposed for estimating heart-disease-related ECG QRS duration. First, raw heartbeats are recognized from the mobile ECG using a support vector machine (SVM). Then, the QRS boundaries are located using a novel pattern recognition approach, multiview dynamic time warping (MV-DTW). To enhance robustness with motion artifacts in the signal, the MV-DTW path distance is also used to quantize heartbeat-specific distortion conditions. Finally, a regression model is trained to transform the mobile ECG QRS duration into the commonly used standard chest ECG QRS durations. Results: With the proposed framework, the performance of ECG QRS duration estimation is very encouraging, and the correlation coefficient, mean error/standard deviation, mean absolute error, and root mean absolute error are 91.2%, 0.4 ± 2.6, 1.7, and 2.6 ms, respectively, compared with the traditional chest ECG-based measurements. Conclusions: Promising experimental results are demonstrated to indicate the effectiveness of the framework. This study will greatly advance machine-learning-enabled ECG data mining towards smart medical decision support.

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