A low-complexity photoplethysmographic systolic peak detector for compressed sensed data
A low-complexity photoplethysmographic systolic peak detector for compressed sensed data
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DOI：
10.1088/1361-6579/ab254b
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发表时间：
2019-06-01
影响因子：
3.2
通讯作者：
Clifford, Gari D.
中科院分区：
文献类型：
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作者：
Da Poian, Giulia;Letizia, Nunzio A.;Clifford, Gari D.
目标：可穿戴技术和信号处理的最新进展使得在日常生活活动中进行健康监测成为可能。尽管新技术允许在小型设备上存储大量数据，但当必须使用具有能源和计算限制的设备传输或处理数据时，仍然存在限制。方法：这项工作的重点是光电体积描记图 (PPG) 低复杂度分析方法的实现和验证，该方法用于通过压缩传感 (CS) 获取压缩 PPG 信号的传感器，并允许准确检测压缩域中的 PPG 收缩峰值。使用三个公共数据集，其中包含来自 600 名心律正常和异常患者的总共约 52 小时的 PPG 信号。峰值由专家手动注释或从带注释的同步心电图得出。主要结果：所提出的方法在三个数据集上实现了合并平均 F1 测量：对于 5% 压缩比 (CR)，为 91% +/- 8%；对于 CR = 70%，为 89% +/- 10%；对于 90% CR，为 82% +/- 12%。使用离线开源峰值检测器对原始未压缩数据进行汇总的平均 F1 测量为 F1 = 91% +/- 11%。与使用解压缩然后进行峰值检测的方法相比，所提出的方法的速度快了大约 100 倍。意义：结果表明，就 F1 测量而言，可以实现与原始未压缩和滤波信号上获得的检测性能相当的检测性能，使得所提出的方法适用于具有能量和计算限制的实时可穿戴系统。
Objective: Recent advances in wearable technologies and signal processing have made it possible to perform health monitoring during everyday life activities. Despite the fact that new technologies allow the storage of large volumes of data on small devices, limitations remain when data have to be transmitted or processed with devices with both energy and computational constraints. Approach: This work focuses on the implementation and validation of a photoplethysmogram (PPG) low-complexity analysis method for sensors that acquire a compressed PPG signal through compressive sensing (CS) and allows for the accurate detection of the PPG systolic peak in the compressed domain. Three public datasets were used consisting of a total of about 52 h of PPG signals from 600 patients with normal and abnormal rhythms. Peaks were manually annotated by experts or derived from the annotated synchronized ECG. Main results: The proposed method achieved a pooled average F1 measure on the three datasets of 91% +/- 8% for a 5% compression ratio (CR), 89% +/- 10% for CR = 70% and 82% +/- 12% for CR of 90%. The pooled average F1 measure on the original uncompressed data using an offline open source peak detector is F1 = 91% +/- 11%. The proposed method is up to similar to 100 times faster with respect to methods using decompression followed by peak detection. Significance: Results demonstrate that it is possible to achieve detection performance, in terms of the F1 measure, comparable with those obtained on the original uncompressed and filtered signal, making the proposed approach appropriate for real-time wearable systems with energy and computation constraints.