RESUMO
Photoplethysmography (PPG) signals have been widely used in evaluating cardiovascular biomarkers, however, there is a lack of in-depth understanding of the remote usage of this technology and its viability for underdeveloped countries. This study aims to quantitatively evaluate the performance of a low-cost wireless PPG device in detecting ultra-short-term time-domain pulse rate variability (PRV) parameters in different postures and breathing patterns. A total of 30 healthy subjects were recruited. ECG and PPG signals were simultaneously recorded in 3 min using miniaturized wearable sensors. Four heart rate variability (HRV) and PRV parameters were extracted from ECG and PPG signals, respectively, and compared using analysis of variance (ANOVA) or Scheirer-Ray-Hare test with post hoc analysis. In addition, the data loss was calculated as the percentage of missing sampling points. Posture did not present statistical differences across the PRV parameters but a statistical difference between indicators was found. Strong variation was found for the RMSSD indicator in the standing posture. The sitting position in both breathing patterns demonstrated the lowest data loss (1.0 ± 0.6 and 1.0 ± 0.7) and the lowest percentage of different factors for all indicators. The usage of commercial PPG and BLE devices can allow the reliable extraction of the PPG signal and PRV indicators in real time.
Assuntos
Fotopletismografia , Postura , Humanos , Frequência Cardíaca/fisiologia , Voluntários Saudáveis , Respiração , EletrocardiografiaRESUMO
Heart Rate Variability (HRV) has become an important risk assessment tool when diagnosing illnesses related to heart health. HRV is typically measured with an electrocardiogram; however, there are multiple studies that use Photoplethysmography (PPG) instead. Measuring HRV with video is beneficial as a non-invasive, hands-free alternative and represents a more accessible approach. We developed a methodology to extract HRV from video based on face detection algorithms and color augmentation. We applied this methodology to 45 samples. Signals obtained from PPG and video recorded an average mean error of less than 1 bpm when measuring the heart rate of all subjects. Furthermore, utilizing PPG and video, we computed 61 variables related to HRV. We compared each of them with three correlation metrics (i.e., Kendall, Pearson, and Spearman), adjusting them for multiple comparisons with the Benjamini-Hochberg method to control the false discovery rate and to retrieve the q-value when considering statistical significance lower than 0.5. Using these methods, we found significant correlations for 38 variables (e.g., Heart Rate, 0.991; Mean NN Interval, 0.990; and NN Interval Count, 0.955) using time-domain, frequency-domain, and non-linear methods.
Assuntos
Eletrocardiografia , Fotopletismografia , Algoritmos , Eletrocardiografia/métodos , Mãos , Frequência Cardíaca/fisiologia , Humanos , Fotopletismografia/métodosRESUMO
Low-amplitude PPG signals are more affected by noise contamination and other undesirable effects because the signal strength is comparable to noise power. Although several authors claim that decreases in the amplitude of the PPG wave should be addressed from signal acquisition and conditioning stages such decreases can also be associated with changes in the patient condition. In that instance, it is important to ensure continuous and reliable HR monitoring which, in turn, depends on how robust is the peak detection method. Numerous efforts have been made to develop algorithms for accurate PPG peak detection under high motion artefact conditions. However, little has been done regarding peak detection in low-amplitude PPG signals. In an attempt to address this issue, a novel and simple peak detection algorithm for PPG signals was proposed. Results show that our method could be a good contribution for robust strategies that can dynamically adapt their peak detection method to circumstances in which a decrease in the amplitude of the PPG signal is expected. Still, more extensive testing under a wide range of conditions (e.g. intensive physical exercise) is needed to perform a rigorous validation.