Arrhythmia is a kind of cardiovascular disease that seriously threats human health. Intelligent analysis of electrocardiogram (ECG) is an effective method for the early prevention and precise treatment to arrhythmia. In clinical ECG waveforms, it is common to see the multi-label phenomenon that one patient would be labeled with multiple types of arrhythmia. However, the current research is mainly to use the multiclass methods to solve the multi-label problem, ignoring the correlations between diseases and causing information loss. Therefore, this article aims: 1) to propose a multi-label feature selection method based on ECG (MS-ECG) and design an evaluation criterion of ECG features based on kernelized fuzzy rough sets so as to choose the optimal feature subset and optimize ECG feature space and 2) to propose the multi-label classification algorithm of arrhythmia based on ECG (MC-ECG) by establishing a multiobjective optimization model. This algorithm based on sparsity constraint explores the correlations between arrhythmia diseases and analyzes the mapping relationship between ECG features and arrhythmia diseases, so that one ECG signal would be automatically and accurately given multiple labels. Through sufficient experiments to prove the feasibility of our methods, we obtain the selected feature subset composed of 23 ECG features by MS-ECG. For the six evaluation criterions of MC-ECG, average precision is 0.8462, hamming loss is 0.1041, ranking loss is 0.1313, one-error is 0.2023, coverage is 0.4015, and micro-F1 is 0.6088. The outcome presents optimal to the current algorithms.

Objective: This paper presents a novel heart sound segmentation algorithm based on Temporal-Framing Adaptive Network (TFAN), including state transition loss and dynamic inference. Methods: In contrast to previous state-of-the-art approaches, TFAN does not require any prior knowledge of the state duration of heart sounds and is therefore likely to generalize to non sinus rhythm. TFAN was trained on 50 recordings randomly chosen from Training set A of the 2016 PhysioNet/Computer in Cardiology Challenge and tested on the other 12 independent databases (2,099 recordings and 52,180 beats). And further testing of performance was conducted on databases with three levels of increasing difficulty (LEVEL-I, -II and -III). Results: TFAN achieved a superior F1 score for all 12 databases except for `Test-B', with an average of 96.72%, compared to 94.56% for logistic regression hidden semi-Markov model (LR-HSMM) and 94.18% for bidirectional gated recurrent neural network (BiGRNN). Moreover, TFAN achieved an overall F1 score of 99.21%, 94.17%, 91.31% on LEVEL-I, -II and -III databases respectively, compared to 98.37%, 87.56%, 78.46% for LR-HSMM and 99.01%, 92.63%, 88.45% for BiGRNN. Conclusion: TFAN therefore provides a substantial improvement on heart sound segmentation while using less parameters compared to BiGRNN. Significance: The proposed method is highly flexible and likely to apply to other non-stationary time series. Further work is required to understand to what extent this approach will provide improved diagnostic performance, although it is logical to assume superior segmentation will lead to improved diagnostics.

Atrial fibrillation (AF) is a progressive disease often initially manifested by intermittent episodes spontaneously terminating, and is an insidious disease. The previous work trained the support vector machine (SVM) classifier on multiple RR interval features. The trained AF detector was tested on the 4th China Physiological Signal Challenge (CPSC2021) database, achieving 97.59% and 89.83% for sensitivity and specificity on dataset1, respectively. The test results were 96.46% and 78.64%on dataset2, respectively. The results show the AF detector based on rhythm is insufficient for the recognition of non-AF. Therefore, this work developed AF scanning algorithm integrating rhythm and P wave information for long-term ECGs. The proposed algorithm is divided into three steps: First, utilize apriori knowledge to locate suspected AF, and then employ a trained rhythm-based AF detector to detect AF. Finally, adopt the dynamic time warping and an Auto-Encoding network to quantize the P wave information to identify the non-AF signal from AF. The results on dataset1 were 97.44% and 98.50%, on dataset2 were 96.13% and 87.42%. This work scanned 11 patients with 24-h paroxysmal AF (PAF). The best result is that the detection accuracy is 99.33%, the false detection is 0.01%, and the worst outcome: the detection accuracy is 88.75%, and the false detection is 14.06%. The results proved that the proposed method could provide reliable scanning for PAF events.

The existing fuzzy rough set (FRS) models all believe that the decision attribute divides the sample set into several “clear” decision classes, and this data processing method makes the model sensitive to noise information when conducting feature selection. To solve this problem, this paper proposes a robust fuzzy rough set model (RS‐FRS) based on representative samples. Firstly, the fuzzy membership degree of the samples is defined to reflect its fuzziness and uncertainty, and RS‐FRS model is constructed to reduce the influence of the noise samples. RS‐FRS model does not need to set parameters for the model in advance and can effectively reduce the complexity of the model and human intervention. On this basis, the related properties of RS‐FRS model are studied, and the sample pair selection algorithm (SPS) based on RS‐FRS is used for feature selection. In this paper, RS‐FRS is tested and analysed on the open 12 datasets. The experimental results show that RS‐FRS model proposed can effectively select the most relevant features and has certain robustness to the noise information. The proposed model has a good applicability for data processing and can effectively improve the performance of feature selection.