Level-crossing processing and deep convolutional neural network for arrhythmia classification in telehealth services.

Area of Science:

• Biomedical Engineering
• Artificial Intelligence in Healthcare
• Signal Processing

Background:

• Telehealthcare relies on wireless biomedical devices, facing challenges in data compression, transmission, security, and processing.
• Efficient and accurate automated diagnosis of cardiac arrhythmias is crucial for effective patient monitoring and prognosis.

Purpose of the Study:

• To propose a novel, efficient, and effective method for automated arrhythmia diagnosis in telehealthcare.
• To achieve real-time data size reduction, computationally efficient signal preconditioning, and low-latency accurate classification.

Main Methods:

• The proposed technique integrates Level-Crossing Analog-Digital Converters (LCADCs), Enhanced Activity Selection Algorithm (EASA), Adaptive-Rate Filtering (ARF), and a 1-D deep convolutional neural network (CNN).
• ECG signals are sampled using the level-crossing concept, followed by QRS-based segmentation and ARF.
• Denoised segments are directly classified by the 1-D CNN without handcrafted feature extraction.

Main Results:

• Achieved an average 4.2-times reduction in acquired samples compared to conventional fixed-rate methods.
• Demonstrated over 7.2-times computational effectiveness in the post-denoising stage due to data dimension reduction.
• Attained a 99% accuracy rate for classifying five clinically important arrhythmia classes from the MIT-BIH dataset with significantly reduced classification latency.

Conclusions:

• The proposed method offers an efficient and effective solution for automated arrhythmia diagnosis in telehealthcare.
• The integration of LCADCs, EASA, ARF, and 1-D CNN significantly improves data compression, processing efficiency, and classification accuracy.
• This approach holds promise for real-time, low-latency, and accurate cardiac monitoring in cloud-connected healthcare environments.