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http://dx.doi.org/10.5392/IJoC.2022.18.2.068

Abnormal Electrocardiogram Signal Detection Based on the BiLSTM Network  

Asif, Husnain (Dept. of Computer Engineering, Kumoh National Institute of Technology)
Choe, Tae-Young (Dept. of Computer Engineering, Kumoh National Institute of Technology)
Publication Information
International Journal of Contents / v.18, no.2, 2022 , pp. 68-80 More about this Journal
Abstract
The health of the human heart is commonly measured using ECG (Electrocardiography) signals. To identify any anomaly in the human heart, the time-sequence of ECG signals is examined manually by a cardiologist or cardiac electrophysiologist. Lightweight anomaly detection on ECG signals in an embedded system is expected to be popular in the near future, because of the increasing number of heart disease symptoms. Some previous research uses deep learning networks such as LSTM and BiLSTM to detect anomaly signals without any handcrafted feature. Unfortunately, lightweight LSTMs show low precision and heavy LSTMs require heavy computing powers and volumes of labeled dataset for symptom classification. This paper proposes an ECG anomaly detection system based on two level BiLSTM for acceptable precision with lightweight networks, which is lightweight and usable at home. Also, this paper presents a new threshold technique which considers statistics of the current ECG pattern. This paper's proposed model with BiLSTM detects ECG signal anomaly in 0.467 ~ 1.0 F1 score, compared to 0.426 ~ 0.978 F1 score of the similar model with LSTM except one highly noisy dataset.
Keywords
Deep Learning; Bidirectional long Short-Term Memory Unit; Signal Processing; Electrocardiogram Signals; Anomaly Detection;
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