Photoplethysmographic (PPG) measurements from ambulatory subjects may suffer from unreliability due to body movements and missing data segments due to loosening of sensor. This paper describes an on-device reliability assessment from PPG measurements using a stack denoising autoencoder (SDAE) and multilayer perceptron neural network (MLPNN). The missing segments were predicted by a personalized convolutional neural network (CNN) and long-short term memory (LSTM) model using a short history of the same channel data. Forty sets of volunteers' data, consisting of equal share of healthy and cardiovascular subjects were used for validation and testing. The PPG reliability assessment model (PRAM) achieved over 95% accuracy for correctly identifying acceptable PPG beats out of total 5000 using expert annotated data. Disagreement with experts' annotation was nearly 3.5%. The missing segment prediction model (MSPM) achieved a root mean square error (RMSE) of 0.22, and mean absolute error (MAE) of 0.11 for 40 missing beats prediction using only four beat history from the same channel PPG. The two models were integrated in a standalone device based on quad-core ARM Cortex-A53, 1.2 GHz, with 1 GB RAM, with 130 MB memory requirement and latency ~0.35 s per beat prediction with a 30 s frame. The present method also provides improved performance with published works on PPG quality assessment and missing data prediction using two public datasets, CinC and MIMIC-II under PhysioNet.

中文翻译：

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使用深度神经网络进行设备上可靠性评估和丢失光电体积描记数据的预测


动态受试者的光电容积描记 (PPG) 测量可能会由于身体运动而变得不可靠，并且由于传感器松动而丢失数据段。本文描述了使用堆栈去噪自动编码器 (SDAE) 和多层感知器神经网络 (MLPNN) 根据 PPG 测量进行的设备上可靠性评估。缺失的片段是通过个性化卷积神经网络（CNN）和长短期记忆（LSTM）模型使用相同通道数据的短期历史来预测的。四十组志愿者的数据（包括同等比例的健康受试者和心血管受试者）被用于验证和测试。 PPG 可靠性评估模型 (PRAM) 使用专家注释数据从总共 5000 个心跳中正确识别可接受的 PPG 心跳，准确率超过 95%。不同意专家注释的比例接近3.5%。对于仅使用来自同一通道 PPG 的 4 个心跳历史记录的 40 个缺失心跳预测，缺失片段预测模型 (MSPM) 的均方根误差 (RMSE) 为 0.22，平均绝对误差 (MAE) 为 0.11。这两个模型集成在一个基于四核 ARM Cortex-A53（1.2 GHz）的独立设备中，具有 1 GB RAM，需要 130 MB 内存，每节拍预测延迟约 0.35 秒（30 秒帧）。本方法还通过使用 PhysioNet 下的两个公共数据集 CinC 和 MIMIC-II 的 PPG 质量评估和缺失数据预测的已发表作品提供了改进的性能。