| 要約: | Cardiovascular diseases are among the major global health problems. For example, sudden cardiac death (SCD) accounts for approximately 4 million deaths worldwide. In particular, an SCD event can subtly change the electrocardiogram (ECG) signal before onset, which is generally undetectable by the patient. Hence, timely detection of these changes in ECG signals could help develop a tool to anticipate an SCD event and respond appropriately in patient care. In this sense, this work proposes a novel computational methodology that combines the maximal overlap discrete wavelet packet transform (MODWPT) with stacked autoencoders (SAEs) to discover suitable features in ECG signals and associate them with SCD prediction. The proposed method efficiently predicts an SCD event with an accuracy of 98.94% up to 30 min before the onset, making it a reliable tool for early detection while providing sufficient time for medical intervention and increasing the chances of preventing fatal outcomes, demonstrating the potential of integrating signal processing and deep learning techniques within computational biology to address life-critical health problems.
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