Improvement of Force-Sensor-Based Heart Rate Estimation Using Multichannel Data Fusion
Improvement of Force-Sensor-Based Heart Rate Estimation Using Multichannel Data Fusion
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DOI：
10.1109/jbhi.2014.2311582
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发表时间：
2015-01-01
影响因子：
7.7
通讯作者：
Leonhardt, Steffen
中科院分区：
文献类型：
--
作者：
Brueser, Christoph;Kortelainen, Juha M.;Leonhardt, Steffen
本文的目的是提出并评估从多个空间分布的力传感器集成到床上的心跳间隔估计算法。此外，使用多通道系统的好处，而不是单一的传感器进行了研究。虽然多渠道优于单一渠道似乎是直观的，但主要的挑战在于找到合适的方法来实际利用这种潜力。为此，提出了两种从多通道振动信号中估计心率的算法，并与单通道传感解决方案进行了比较。第一种方法通过分析从单个信道的平均频谱计算得到的倒谱，而第二种方法将贝叶斯融合应用于三个区间估计，如自相关，应用于每个信道。这项评估是基于28个夜间睡眠的实验室记录，在此期间，使用基于聚偏氟乙烯的八通道传感器阵列来获取心脏振动信号。被招募的患者患有不同程度的睡眠障碍。从传感器阵列数据中，通过对通道进行平均，得出一个虚拟的单通道信号进行比较。单通道结果实现了2.2%的节拍间隔误差，覆盖率（即可以分析的记录百分比）为68.7%。相比之下，最佳多通道结果的平均误差和覆盖率分别为1.0%和81.0%。这些结果显示，与单通道结果相比，这两个指标在统计学上有显著改善（p < 0.05）。
The aim of this paper is to present and evaluate algorithms for heartbeat interval estimation from multiple spatially distributed force sensors integrated into a bed. Moreover, the benefit of using multichannel systems as opposed to a single sensor is investigated. While it might seem intuitive that multiple channels are superior to a single channel, the main challenge lies in finding suitable methods to actually leverage this potential. To this end, two algorithms for heart rate estimation from multichannel vibration signals are presented and compared against a single-channel sensing solution. The first method operates by analyzing the cepstrum computed from the average spectra of the individual channels, while the second method applies Bayesian fusion to three interval estimators, such as the autocorrelation, which are applied to each channel. This evaluation is based on 28 night-long sleep lab recordings during which an eight-channel polyvinylidene fluoride-based sensor array was used to acquire cardiac vibration signals. The recruited patients suffered from different sleep disorders of varying severity. From the sensor array data, a virtual single-channel signal was also derived for comparison by averaging the channels. The single-channel results achieved a beat-to-beat interval error of 2.2% with a coverage (i.e., percentage of the recording which could be analyzed) of 68.7%. In comparison, the best multichannel results attained a mean error and coverage of 1.0% and 81.0%, respectively. These results present statistically significant improvements of both metrics over the single-channel results (p < 0.05).