Neuromorphic Parallel Computing Hardware Based on Quantum Dots for 12-Lead Electrocardiogram Monitoring
The 12-lead electrocardiogram (ECG) is indispensable for the initial diagnosis of cardiac conditions, yet existing neuromorphic hardware for multi-lead ECG monitoring requires multiple array circuits and two operational processes, imposing severe constraints on device consistency and diagnostic accuracy. This study introduces a neuromorphic parallel computing hardware architecture based on quantum dot synaptic transistors that leverages trap and surface electric field effects to enable 12-lead ECG monitoring within a single array circuit, eliminating the need for twelve separate circuits. The system concurrently processes multiple ECG signals and produces final outputs without external computing or control circuits. A 12-transistor array, termed STAC, directly processes one-dimensional ECG data without additional conversion circuits, integrating a feature extraction layer at the pixel level and a feature fusion layer at the circuit level. Classification of ECG signals from the MIT-BIH Arrhythmia Database and the Chinese Twelve-Lead ECG Challenge Database yields a training accuracy exceeding 98%. A five-class ECG signal classification task achieves 96.2% recognition accuracy, with a 5×5 confusion matrix confirming high classification precision across normal (N) and four abnormal categories (A, V, L, R). The architecture accurately detects myocardial infarction by fine-tuning internal weights, demonstrating proficiency in monitoring abnormal ECG signals. This advancement offers a compact, low-cost solution for wearable and portable 12-lead ECG monitoring devices, enabling real-time cardiac assessment with reduced hardware complexity and enhanced diagnostic reliability.