| Abstrakt: |
Electrocardiogram (ECG) signal analysis is a crucial tool for enhancing the efficacy of clinical diagnosis, particularly in detecting arrhythmias. However, its performance tends to degrade under the inter-patient paradigm, especially for minority sample categories. To address this issue and enhance the detection performance of these less represented classes within the inter-patient test protocol, this paper proposes a novel hybrid framework. This framework integrates specialized blocks from convolutional networks, namely DeepArr CNN, with sequence-to-sequence BiLSTM models. The proposed approach involves extracting local ECG features from a sequence of heartbeats utilizing DeepArr CNN. These extracted feature maps are then fed into the RNN encoder-decoder to facilitate fusion with the feature maps of neighboring heartbeats. Our proposed method, which adhered to the AMII standard of the MIT-BIH arrhythmia database and operated within the inter-patient paradigm, achieved impressive accuracy rates. Specifically, it yielded accuracy rates of 99.92% and 99.81% for three and five class scenarios, respectively. The performance measures across different classes are as follows: for the N class, sensitivity (SEN) is 99.81%, positive predictive value (PPV) is 99.73%, and specificity (SPEC) is 97.81%; for the S class, SEN is 92.97%, PPV is 96.01%, and SPEC is 99.85%; for the V class, SEN is 99.97%, PPV is 99.26%, and SPEC is 99.95%; and for the F class, SEN is 97.42%, PPV is 95.70%, and SPEC is 99.97%. Even when dealing with an unbalanced dataset, our proposed solution consistently yields remarkable outcomes. Specifically, it achieves an accuracy rate of 99.24% in the three-class scenario and 98.75% in the five-class scenario. These experimental results underscore the effectiveness of our S2SDeepArr model for ECG Classification under the inter-patient paradigm, as demonstrated across various experimental cases. [ABSTRACT FROM AUTHOR] |
