Abdelazez Mohamed, Rajan Sreeraman, Chan Adrian D C
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5398-5401. doi: 10.1109/EMBC44109.2020.9175813.
Atrial Fibrillation (AF) is a cardiac condition resulting from uncoordinated contraction of the atria which may lead to an increase in the risk of heart attacks, strokes, and death. AF symptoms may go undetected and may require longterm monitoring of electrocardiogram (ECG) to be detected. Long-term ECG monitoring can generate a large amount of data which can increase power, storage, and the wireless transmission bandwidth of monitoring devices. Compressive Sensing (CS) is compression technique at the sampling stage which may save power, storage, and wireless bandwidth of monitoring devices. The reconstruction of compressive sensed ECG is a computationally expensive operation; therefore, detection of AF in compressive sensed ECG is warranted. This paper presents preliminary results of using deep learning to detect AF in deterministic compressive sensed ECG. MobileNetV2 convolutional neural network (CNN) was used in this paper. Transfer learning was utilized to leverage a pre-trained CNN with the final two layers retrained using 24 records from the Long-Term Atrial Fibrillation Database. The Short-Term Fourier Transform was used to generate spectrograms that were fed to the CNN. The CNN was tested on the MIT-BIH Atrial Fibrillation Database at the uncompressed, 50%, 75%, and 95% compressed ECG. The performance of the CNN was evaluated using weighted average precision (AP) and area under the curve (AUC) of the receiver operator curve (ROC). The CNN had AP of 0.80, 0.70, 0.70, and 0.57 at uncompressed, 50%, 75%, and 95% compression levels. The AUC was 0.87, 0.78, 0.79, and 0.75 at each compression level. The preliminary results show promise for using deep learning to detect AF in compressive sensed ECG.Clinical Relevance-This paper confirms that AF can be detected in compressive sensed ECG using deep learning, This will facilitate long-term ECG monitoring using wearable devices and will reduce adverse complications resulting from undiagnosed AF.
心房颤动（AF）是一种由心房不协调收缩引起的心脏疾病，可能会导致心脏病发作、中风和死亡风险增加。AF症状可能未被察觉，可能需要长期监测心电图（ECG）才能检测到。长期ECG监测会产生大量数据，这会增加监测设备的功率、存储和无线传输带宽。压缩感知（CS）是一种在采样阶段的压缩技术，它可以节省监测设备的功率、存储和无线带宽。压缩感知ECG的重建是一项计算成本高昂的操作；因此，有必要在压缩感知ECG中检测AF。本文展示了使用深度学习在确定性压缩感知ECG中检测AF的初步结果。本文使用了MobileNetV2卷积神经网络（CNN）。利用迁移学习来利用一个预训练的CNN，其最后两层使用来自长期心房颤动数据库的24条记录进行重新训练。使用短时傅里叶变换生成频谱图并输入到CNN中。该CNN在未压缩、50%、75%和95%压缩的ECG上对麻省理工学院-贝丝以色列女执事医疗中心心房颤动数据库进行了测试。使用加权平均精度（AP）和接收器操作曲线（ROC）的曲线下面积（AUC）来评估CNN的性能。在未压缩、50%、75%和95%的压缩水平下，CNN的AP分别为0.80、0.70、0.70和0.57。在每个压缩水平下，AUC分别为0.87、0.78、0.79和0.75。初步结果显示了使用深度学习在压缩感知ECG中检测AF的前景。临床相关性——本文证实了使用深度学习可以在压缩感知ECG中检测到AF，这将便于使用可穿戴设备进行长期ECG监测，并将减少未诊断AF导致的不良并发症。
