Working principle of PPG sensors [19]. 

Working principle of PPG sensors [19]. 

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Heart Rate Variability (HRV) is an important tool for the analysis of a patient’s physiological conditions, as well a method aiding the diagnosis of cardiopathies. Photoplethysmography (PPG) is an optical technique applied in the monitoring of the HRV and its adoption has been growing significantly, compared to the most commonly used method in medi...

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... working principle of the PPG sensor is based on the emission of infrared light by an LED which penetrates the skin and blood vessels. This light is captured by the detector to measure the blood stream, as can be observed in Figure 6. The results of the PPG signal depend primarily on the flow of blood and oxygen to the capillary vessels in each heartbeat [19]. Theoretically, the PPG signal is formed by two components: (1) the DC offset, which represents the constant absorption of light passing through the tissues; and (2) the AC component generated by heartbeats affecting blood volume when light traverses the artery ...

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... To eliminate the chances of human errors, hospitals started using devices employing contact photoplethysmography (PPG) techniques (Moraes et al., 2018). With every cycle of blood circulation (heart beat), blood circulates through the body. ...
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Keywords: Remote heart rate detection, Video-based heart rate detection Abstract: Video-based remote heart rate detection is a promising technology that can offer convenient and low-cost heart rate monitoring within, but not limited to, the clinical environment, especially when attaching electrodes or pulse oximeters on a person is not possible or convenient. In this work, we examined common steps used in video-based remote heart rate detection algorithms, in order to evaluate their effect on the overall performance of the remote heart rate detection pipeline. Various parameters of the examined methods were evaluated on three public and one proprietary dataset in order to establish a video-based remote heart rate detection pipeline that provides the most balanced performance across various diverse datasets. The experimental evaluation demonstrated the effect and contribution of each step and parameter set on the estimation of the heart rate, resulting in an optimal configuration that achieved a best RMSE value of 9.51.
... e living environment is determined by the assistive living technology that persons require, such as personal alarms, sensor mats, cameras, and other similar devices. ese sensors can be connected to wireless transmission modalities such as "RFID," "NFC," "Bluetooth," "BLE," "Wi-Fi," and "ZigBee" to enable transmission of the measured data to the outside world [20]. To facilitate compatibility and connection with software, the majority of these sensing devices are built to digital communications speci cations. ...
... Usage of the IoT in healthcare[20]. ...
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Chronic diabetes among adults is a public health concern and clinicians are trying to implement new strategies to effectively manage the disease. Traditionally, healthcare professionals are used to monitor and track the lab reports of patients. After that, they used to provide respective medicines and lifestyle plans to manage the chronic disease. e lifestyle of the patients and access to safe and secure food products is also responsible for developing chronic diseases. us, the Internet of ings (IoT) has taken an utmost interest in managing diabetes. is research is going to analyze the accuracy of IoT in assisting chronic diabetes management and determining food safety. To accomplish the research objectives, the researchers performed a linear regression analysis to understand whether IoT devices and Artificial Intelligence (AI) assist in assessing food safety and diabetes management. e independent variables selected were lab test values, treatment records, epoch size of AI, and image resolution of the training dataset. Dependent variables were the accuracy of IoT. Here, the accuracy of IoT and AI has been determined. Moreover, the accuracy of clinicians in diabetes management has been observed. It has been found that clinicians have high variance in accuracy (max 99%) whereas machines have limited variance in accuracy (max. 98%). Secondary research identified that clinicians need to be involved along with IoT devices for better management of this chronic disease and help patients by providing the safest food options.
... The PPG and the motion artefacts frequency typically range from 0.5 to 4 Hz and 0.1 to 10 Hz, respectively [18]. This bandwidth overlap hinders the process of obtaining a clean signal using ordinary filtering. ...
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Heart Rate Variability (HRV) evaluates the autonomic nervous system regulation and can be used as a monitoring tool in conditions such as cardiovascular diseases, neuropathies and sleep staging. It can be extracted from the electrocardiogram (ECG) and the photoplethysmogram (PPG) signals. Typically, the HRV is obtained from the ECG processing. Being the PPG sensor widely used in clinical setups for physiological parameters monitoring such as blood oxygenation and ventilatory rate, the question arises regarding the PPG adequacy for HRV extraction. There is not a consensus regarding the PPG being able to replace the ECG in the HRV estimation. This work aims to be a contribution to this research area by comparing the HRV estimation obtained from simultaneously acquired ECG and PPG signals from forty subjects. A peak detection method is herein introduced based on the Hilbert transform: Hilbert Double Envelope Method (HDEM). Two other peak detector methods were also evaluated: Pan-Tompkins and Wavelet-based. HRV parameters for time, frequency and the non-linear domain were calculated for each algorithm and the Pearson correlation, T-test and RMSE were evaluated. The HDEM algorithm showed the best overall results with a sensitivity of 99.07% and 99.45% for the ECG and the PPG signals, respectively. For this algorithm, a high correlation and no significant differences were found between HRV features and the gold standard, for the ECG and PPG signals. The results show that the PPG is a suitable alternative to the ECG for HRV feature extraction.
... In terms of cardiac cycle time t 3 , all the participants except subject 8 exhibited a significant response to the oxygen-deficiency condition. The decrease of cardiac cycle t 3 meant an increase in heart rate, reflecting the growth of blood flow in the PPG signal due to contraction of the left ventricle of the heart [36]. As for the rising slope of the PPG signal, the positive-sense effect of the oxygen-deficiency condition was only observed for subject 2 (P < 0.001), subject 7 (P < 0.05), and subject 10 (P < 0.05). ...
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Oxygen-deficiency is a cause of fatalities in confined-space workplaces. Most research and projects have been conducted to reduce work-related accidents by external measurements, while works addressing the early warning of workers’ hypoxia state through a bio-electrical signal have rarely been conducted. In this paper, we present a hypoxia detection system based on non-invasive photoplethysmograph (PPG) measurement using machine-learning (ML) algorithms. The PPG signals obtained from 22 subjects underwent preprocessing and features extraction steps. Time-domain features, rising and falling slopes, and amplitude parameters were adopted to train and test the ML algorithms. In addition, an Internet of Things- (IoT) based smart wearable device and monitoring system were developed to measure the vital parameters of workers in confined spaces. Cardiac cycle time of all the participants except subject 8 decreased significantly (P < 0.01) throughout the oxygen-deficiency trial. The PPG signal complexity essentially decreased (P = 0.006) when the concentration of environmental oxygen declined. The DT algorithm with nine input parameters outperformed the other algorithms in prediction accuracy (Acc = 0.9). The results show that the features extracted from the PPG signal can be adopted as important indicators to revealing the hypoxia state of workers. The device and system automatically measure and analyze the PPG signal with the location details of caregivers and notify the monitoring staff in the case of an emergency.
... The AC component represents blood volume cardiac variation in each heartbeat, and it is attributed to the pulsatile behavior of the heart [28]. ...
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In this work, the authors investigate the cuff-less estimation of continuous BP through pulse transit time (PTT) and heart rate (HR) using regression techniques, which is intended as a first step towards continuous BP estimation with a low error, according to AAMI guidelines. Hypertension (the ‘silent killer’) is one of the main risk factors for cardiovascular diseases (CVDs), which are the main cause of death worldwide. Its continuous monitoring can offer a valid tool for patient care, as blood pressure (BP) is a significant indicator of health and, using it together with other parameters, such as heart and breath rates, could strongly improve prevention of CVDs. The novelties introduced in this work are represented by the implementation of pre-processing and by the innovative method for features research and features processing to continuously monitor blood pressure in a non-invasive way. Currently, invasive methods are the only reliable methods for continuous monitoring, while non-invasive techniques measure the values every few minutes. The proposed approach can be considered the first step for the integration of these types of algorithms on wearable devices, in particular on those developed for the SINTEC project.
... The DC component is much slower and depends on respiration, vascular activity and thermoregulation [24]. Besides its fundamental frequency, PPG frequencies can range from 0.5 to 4.0 Hz [28], and, by also being a low-amplitude signal, it can be affected by various types of noise such as motion artefacts, respiratory wandering and powerline interference. Important curve parameters include: the pulse width, defined as the width of the curve at half of the systolic peak [29]; the pulse area, which is the total area covered by the PPG curve [29]; the peak-to-peak interval, the time interval between two consecutive systolic peaks [29]; and the pulse interval, the duration of a PPG waveform [29]. ...
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The availability of low-cost biomedical devices has driven a growing interest in the use of physiological signals for mental and emotional health research. Due to their potential for integration in discrete wearable form factors, Photoplethysmography (PPG) and Electrodermal Activity (EDA) are particularly popular, especially in out-of-the-lab experiments. Although high-resolution data acquisition should be a priority, the sampling rate can greatly affect the power consumption and memory storage of the devices in long-term recordings. Moreover, systems with shared computational resources that simultaneously monitor different signals, can also have communication channel bandwidth constraints that limit the sampling rate. This work seeks to evaluate how the sampling rate and interpolation affect the signal quality of PPG and EDA signals, in terms of waveform morphology and feature extraction capabilities. We study the minimum sampling rate requirements for each signal, as well as the impact of interpolation methods on signal waveform reconstruction. Using a previously recorded dataset with signals originally recorded at 1 kHz, we simulate multiple lower sampling rates. Results show that for PPG a 50 Hz sampling rate with quadratic or cubic interpolation can achieve a temporal resolution identical to that of a 1 kHz acquisition, while for EDA the same can be said but with a 10 Hz sampling rate. Other recommendations are also proposed depending on the signal application.
... Several reviews on the application of PPG signals have been carried out. Some of them focus on the specific medical use of PPG like pulse rate [3], blood pressure [4,5], atrial fibrillation [6,7], circulatory monitoring [8], nociception [9], or on the specific placement of PPG [10,11], others instead focused on reviewing the way that the signal has been analyzed [7,[12][13][14][15][16] or the type of the sensor [17]. More than a decade ago, J. Allen [2] published an interesting review about the applications PPG in clinical physiological measurement. ...
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Recent research indicates that Photoplethysmography (PPG) signals carry more information than oxygen saturation level (SpO2) and can be utilized for affordable, fast, and noninvasive healthcare applications. All these encourage the researchers to estimate its feasibility as an alternative to many expansive, time-wasting, and invasive methods. This systematic review discusses the current literature on diagnostic features of PPG signal and their applications that might present a potential venue to be adapted into many health and fitness aspects of human life. The research methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines 2020. To this aim, papers from 1981 to date are reviewed and categorized in terms of the healthcare application domain. Along with consolidated research areas, recent topics that are growing in popularity are also discovered. We also highlight the potential impact of using PPG signals on an individual's quality of life and public health. The state-of-the-art studies suggest that in the years to come PPG wearables will become pervasive in many fields of medical practices, and the main domains include cardiology, respiratory, neurology, and fitness. Main operation challenges , including performance and robustness obstacles, are identified.
... Heart rate was measured using Elitech ® FOX-1 pulse oximetry (Surabaya, Indonesia) by recording blood oxygenation pulsations. The photoplethysmography (PPG) method is used to detect heart rate variability by emitting or reflecting light rays into the bloodstream; then, the changes in light energy are read as cardiac cycles in relation to systole and diastole [39,40], and PPG signals can be recorded from the finger to calculate reliable heart rate variability estimates [41]. A previous study demonstrated that PPG provides accurate interpulse intervals to measure heart rate variability under ideal conditions [42]. ...
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Traveling with children with autism can be very challenging for parents due to their reactions to sensory stimuli resulting in behavioral problems, which lead to self-injury and danger for themselves and others. Deep pressure was reported to have a calming effect on people with autism. This study was designed to investigate the physiological effect of deep pressure, which is an autism hug machine portable seat (AHMPS) in children with autism spectrum disorders (ASD) in public transportation settings. The study was conducted with 20 children with ASD (16 boys and 4 girls) at the Semarang Public Special School with an age ranging from 4 to 13 years (mean 10.9 ± 2.26 years), who were randomly assigned into two groups. The experiment consisted of group I who used the AHMPS inflatable wraps model and group II who used the AHMPS manual pull model. Heart rate (HR) and skin conductance (SC) were analyzed to measure the physiological calming effect using pulse oximeter oximetry and a galvanic skin response (GSR) sensor. Heart rate was significantly decreased during the treatment compared to the baseline (pre-test) session in group I (inflating wrap model) with p = 0.019, while no change of heart rate variability (HRV) was found in group II (manual pull model) with p = 0.111. There was no remaining effect of deep pressure using the HRV indicator after the treatment in both groups (group I with p = 0.159 and group II with p = 0.566). GSR captured the significant decrease in skin conductance during the treatment with p < 0.0001 in group I, but no significant decrease was recorded in group II with p = 0.062. A skin conductance indicator captured the remaining effect of deep pressure (after the treatment); it was better in group I (p = 0.003) than in group II (p = 0.773). In conclusion, the deep pressure of the AHMPS inflating wrap decreases physiological arousal in children with ASD during traveling.
... The function of photoplethysmography has been classified into two types, reflection or transmission of light over or by a specific part of the body. Here [64], PPG technical tool has been used for respiratory and heart rate acquisition, instead of using other technique like ECG. As a result, the safest extraction of respiratory data is retrieved through PPG waveforms, which evaluates values better than ECG signal. ...
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The novel coronavirus, renamed SARS-CoV-2 and most commonly referred to as COVID-19, has infected nearly 44.83 million people in 224 countries and has been designated SARS-CoV-2. In this study, we used ‘web of Science’, ‘Scopus’ and ‘goggle scholar’ with the keywords of “SARS-CoV-2 detection” or “coronavirus 2019 detection” or “COVID 2019 detection” or “COVID 19 detection” “corona virus techniques for detection of COVID-19”, “audio techniques for detection of COVID-19”, “speech techniques for detection of COVID-19”, for period of 2019–2021. Some COVID-19 instances have an impact on speech production, which suggests that researchers should look for signs of disease detection in speech utilising audio and speech recognition signals from humans to better understand the condition. It is presented in this review that an overview of human audio signals is presented using an AI (Artificial Intelligence) model to diagnose, spread awareness, and monitor COVID-19, employing bio and non-obtrusive signals that communicated human speech and non-speech audio information is presented. Development of accurate and rapid screening techniques that permit testing at a reasonable cost is critical in the current COVID-19 pandemic crisis, according to the World Health Organization. In this context, certain existing investigations have shown potential in the detection of COVID 19 diagnostic signals from relevant auditory noises, which is a promising development. According to authors, it is not a single “perfect” COVID-19 test that is required, but rather a combination of rapid and affordable tests, non-clinic pre-screening tools, and tools from a variety of supply chains and technologies that will allow us to safely return to our normal lives while we await the completion of the hassle free COVID-19 vaccination process for all ages. This review was able to gather information on biomedical signal processing in the detection of speech, coughing sounds, and breathing signals for the purpose of diagnosing and screening the COVID-19 virus.
... The framework used in TEA, which we have made available and adaptable as open-source (Sheikh, Shah, Levantsevych, et al., 2020), may also apply in the signal processing of other physiological signals that are also overly sensitive to noise, including ballistocardiograph, seismocardiograph, and photoplethysmography (Inan et al., 2014(Inan et al., , 2018Moraes et al., 2018). The modular form of the algorithm can also accommodate modifications in its various stages. ...
Article
Pre-ejection period (PEP) is an index of sympathetic nervous system activity that can be computed from electrocardiogram (ECG) and impedance cardiogram (ICG) signals, but sensitive to speech/motion artifact. We sought to validate an ICG noise removal method, three-stage ensemble-average algorithm (TEA), in data acquired from a clinical trial comparing active versus sham non-invasive vagal nerve stimulation (tcVNS) after standardized speech stress. We first compared TEA's performance versus the standard conventional ensemble-average algorithm (CEA) approach to classify noisy ICG segments. We then analyzed ECG and ICG data to measure PEP and compared group-level differences in stress states with each approach. We evaluated 45 individuals, of whom 23 had post-traumatic stress disorder (PTSD). We found that the TEA approach identified artifact-corrupted beats with intraclass correlation coefficient > 0.99 compared to expert adjudication. TEA also resulted in higher group-level differences in PEP between stress states than CEA. PEP values were lower in the speech stress (vs. baseline rest) group using both techniques, but the differences were greater using TEA (12.1 ms) than CEA (8.0 ms). PEP differences in groups divided by PTSD status and tcVNS (active vs. sham) were also greater when using the TEA versus CEA method, although the magnitude of the differences was lower. In conclusion, TEA helps to accurately identify noisy ICG beats during speaking stress, and this increased accuracy improves sensitivity to group-level differences in stress states compared to CEA, suggesting greater clinical utility.