The current approaches for cardiac disorder detection, which often require computationally expensive deep learning architectures

The gap

Evidence profile

Sourced from the stated research gap and future work of the source papers, classified as general, spanning 3 journals.

Supporting evidence — 3 representative gaps

• Accurate and Low-Cost Cardiac Disorder Detection from Wearable Phonocardiogram Signals Using Hybrid Feature Selection and Machine Learning (2026) · Biosensors · doi

  The paper identifies a gap in the current approaches for cardiac disorder detection, which often require computationally expensive deep learning architectures. The study highlights the need for a more efficient and effective approach for feature selection and classification. The current methods may not provide high accuracy and robustness for clinical applications.

  generalstated research gap
  Keywords: paper identifies gap current approaches cardiac disorder detection
• ECG Signal Classification Method Based on Structural Risk Minimization (2026) · EAI Endorsed Transactions on Pervasive Health and Technology · doi

  leverages both This study proposes a CNN network integrated with the SRM the feature criterion algorithm, which extraction capabilities of CNN and the SRM algorithm's strong generalization ability for unknown data classification. Test results from the MIT-BIH Arrhythmia Database demonstrate that the proposed network achieves higher accuracy (F1 score: 87.4, accuracy: 88.2%) compared to CNN networks without the SRM algorithm. These results indicate its potential as a diagnostic tool in clinical practice. Future work includes:(1) further testing the accuracy of the proposed model on other data sets to verify the generalization of the proposed model;(2) further exploring other deep learning feature extraction methods. Future work includes: (1) further testing the accuracy of the proposed model on other data sets to verify the generalization of the proposed model, such as incorporating diverse physiological databases to assess robustness across varying patient demographics; (2) further exploring other deep learning feature extraction methods, including attention mechanisms or transformer-based approaches, to potentially improve reduce computational overhead. (3) Investigating the optimization of model hyperparameters and training strategies to enhance efficiency for real-time clinical applications, addressing potential challenges like latency and resource constraints. (4) Collaborating with medical institutions to conduct pilot studies, ensuring the model's practical utility and ethical considerations in diagnostic workflows, while also exploring integration with wearable devices for continuous monitoring. classification performance and

  generalfuture workevidence 5/5
  Keywords: model proposed accuracy further feature algorithm extraction generalization exploring network classification potential diagnostic clinical future
• An interpretable multi-scale hybrid attention network (MSHAN) for explainable ECG arrhythmia classification (2026) · Frontiers in Artificial Intelligence · doi

  MSHAN demonstrated competitive classification performance and enhanced interpretability for ECG arrhythmia classification, achieving an accuracy of 96.25% along with high precision, recall, and F1 scores. The model’s hybrid architecture combines the advantages of multi-scale feature extraction, hierarchical attention, and GRU-based temporal modeling, enabling it to surpass traditional approaches such as 1D CNN, LSTM, BiLSTM, GRU, ResNet-1D, and MLP. In clinical settings, this strategy would reduce ECG signal processing time by accurately detecting cardiovascular abnormalities and help lower medical errors,

  generalfuture workevidence 5/5
  Keywords: classification mshan competitive performance enhanced interpretability arrhythmia achieving accuracy along high precision recall scores model

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