Accurate ECG Classification Based on Spiking Neural Network and Attentional Mechanism for Real-Time Implementation on Personal Portable Devices

Abstract

Electrocardiogram (ECG) heartbeat classification plays a vital role in early diagnosis and effective treatment, which provide opportunities for earlier prevention and intervention. In an effort to continuously monitor and detect abnormalities in patients’ ECG signals on portable devices, this paper present a lightweight ECG heartbeat classification method based on a spiking neural network (SNN), a relatively shallow SNN model integrated with a channel-wise attentional module. We further explore the best-optimized architecture, which benefits from leveraging the full advantages of the SNN potential with the attention mechanism to process the classification task at low power and capture prominent features concerning the time, morphology, and multi-channel representations of the ECG signal. Results show that our model achieves overall classification accuracy of 98.26%, sensitivity of 94.75%, and F1 score of 89.09% on the MIT-BIH database, with energy consumption of 346.33 μJ per beat and runtime of 1.37 ms. Moreover, we have conducted multiple experiments to compare against current state-of-the-art methods using their assessment strategies to evaluate our model implementation on FPGA. So far, our work achieves comparable overall performance with all the literature in terms of classification accuracy, energy consumption, and real-time capability.

Integrated Memristor Network for Physiological Signal Processing

“…Moreover, artificial neurons also play an important role in SNN, and the processing of time series by SNN has a wide range of applications in physiological signal processing. [ 55 ] The basic requirement of artificial neurons is to realize the potential accumulation processes and spike generation functions. Many neuron models have been proposed and used based on biological rationality.…”

“…Reproduced under the terms of the CC‐BY license. [ 55 ] Copyright 2022, The authors, published by MDPI. d) Schematic of amnestic blocker simulated biological synapse.…”

“…In addition to the EEG mentioned above, ECG, EMG, EOG, and PPG are also time‐series data; thus, SNN has a strong potential for processing these physiological signals. Studies have used SNN to classify ECG signals with promising results [ 55 ] (Figure 11c). For these reasons, researchers are increasingly interested in neuromorphic computing, and many studies attempt to implement SNN on computational chips in real time and efficiently.…”

“…c) Framework of SNN system for automatic diagnosis of ECG arrhythmias. Reproduced under the terms of the CC-BY license [55]. Copyright 2022, The authors, published by MDPI.…”

Optimal Mapping of Spiking Neural Network to Neuromorphic Hardware for Edge-AI

“…The spiking neural network (SNN), known as the third generation of the neural network, has been introduced into many application fields including electrocardiogram heartbeat classification [ 3 ], object recognition [ 4 ], waveform analysis [ 5 ], odor data classification [ 6 ], and image classification [ 7 ]. SNN has the potential to effectively process spatial-temporal information.…”