IEEE Transactions on Instrumentation and Measurement · 2019 · 301 citations · 42 references
ComputingCardiologyComputer ScienceBiomedical Signal ProcessingFeature EngineeringMachine LearningCardiac ArrhythmiasDeep LearningElectrocardiographyPattern RecognitionRepresentation LearningSupport Vector MachinesAccuracyArrhythmia RecognitionEcg Feature ExtractionEcg Signal ReconstructionElectrocardiogram Feature ExtractionLearned Feature RepresentationsLong Short-term Memory NetworksSupport Vector Machine Classification
The study proposes a deep learning framework combining an LSTM‑based auto‑encoder with an SVM to classify ECG arrhythmia signals. The method uses an LSTM‑based auto‑encoder to extract high‑level features from ECG signals, then feeds these features into an SVM classifier. The approach achieved 99.74 % accuracy, 99.35 % sensitivity, and 99.84 % specificity on beat‑based cross‑validation, and 85.20 % accuracy, 62.99 % sensitivity, and 90.75 % specificity on record‑based cross‑validation, demonstrating superior performance over traditional methods.
This paper introduces a novel deep learning-based algorithm that integrates a long short-term memory (LSTM)-based auto-encoder (AE) network with support vector machine (SVM) for electrocardiogram (ECG) arrhythmias classification. The LSTM-based AE network (LSTM-AE) is used to learn the features from ECG arrhythmias signals, and the SVM is used to classify those signals from the learned features. The LSTM-AE consists of an encoder model, which extracts high-level feature information from ECG arrhythmias signals through LSTM network, and a decoder model which outputs reconstruct ECG arrhythmias signals from high-level features through LSTM network. Experiments show that the proposed method can learn better features than the traditional method without any prior knowledge, presenting a good potential for the ECG arrhythmias classification. In the classification of five heartbeats types, including normal, left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature complexes (APC), premature ventricular contractions (PVC), the proposed method achieved average accuracy, sensitivity, and specificity of 99.74%, 99.35%, and 99.84%, respectively, in the beat-based cross-validation approach, and 85.20%, 62.99%, and 90.75%, respectively, in the record-based cross-validation approach, in public MIT-BIH Arrhythmia Database. While based on the Advancement of Medical Instrumentation (AAMI) standards, the proposed method achieved average accuracy, sensitivity, and specificity of 99.45%, 98.63%, and 99.66%, respectively, in the beat-based cross-validation approach.
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