Stress recognition using photoplethysmogram signal

This study proposed a novel method of stress recognition using photopletysmogram (PPG) signal. PPG devices are now widely used because it is convenient, low powered, cheap and also easy to handle due to its small size. A total of 5 subjects were involved in this study. The PPG signals were taken in resting condition using pulse oximetry. The subject then goes through a stressor test in order to record the physiological changes. The data were collected before and after the test was conducted and later extracted. These samples were then categorised using classification techniques to differentiate between normal and stress condition. Based on the experimentation results, the systolic peak value differences of normal and stress conditions are evident.Therefore, the outcome of this study suggest the reliability of implementing PPG signal for stress recognition. Keywords: Photoplethysmogram (PPG), stress, systoli

Machine learning algorithm development of SPO2 sensor for improved robustness in wearables

Wearable devices application in the digital measurement of health has gained attention by researchers. These devices allow for data acquisition during real-life activities, resulting in higher data availability. They often include photoplethysmography (PPG) sensors, the sensor behind pulse oximetry. Pulse oximetry is a non-invasive method for continuous oxygen saturation (SpO2) measurements, a standard monitor for anesthesia procedures, an essential tool for managing patients undergoing pulmonary rehabilitation and an effective method for assessing sleep-disordered breathing. However, the current market focuses on heart rate measurements and lacks the robustness of clinical applications for SpO2 assessment. In addition, the most common obstacle in PPG measurements is the signal quality, especially in the form of motion artifacts. Thus, this work aims at increasing the clinical robustness in this devices by evaluating its quality and then extracting relevant metrics. Firstly, a data acquisition protocol was developed, focused on acquiring data during daily activities. This resulted in a dataset with different signal qualities, which was manually annotated to be used as the base for the Machine Learning models. A second protocol was also developed especially designed for the extraction of the SpO2 measurement. Several Machine Learning models were developed to evaluate the signal in three distinct qualities (corrupted, suboptimal, optimal) in real time. A Random Forest classifier achieved accuracies of 79% and 80% for the binary models capable of differentiating between usable and unusable signals, and accuracies of 74% and 80% when distinguishing between optimal and suboptimal signals, for the two utilized channels. The multi-class models achieved accuracies of 66% and 65% for the two utilized channels. Three clinically relevant metrics were also extracted from the PPG signal: heart rate, respiratory rate and SpO2. The heart rate and respiratory rate algorithms resulted in performances similar to the ones found in the literature and in other devices currently on the market. However, while promising, more data is needed to reach statistical significance for the SpO2 measurement.A monitorização do estado de saúde de pacientes em ambulatório utilizando dispositivos wearables tem vindo a ser cada vez mais investigada. Estes dispositivos permitem uma aquisição de dados durante o dia a dia, resultando num maior conjunto de dados. Frequentemente, estes dispositivos incluem fotopletismógrafos (PPG), o sensor por detrás da oximetria de pulso. A oximetria de pulso é um método não invasivo para a medição da saturação de oxigénio no sangue (SpO2) de forma contínua. É um equipamento padrão para procedimentos com anestesia, uma ferramenta essencial para monitorizar pacientes em reabilitação pulmonar e um método eficaz para avaliar respiração desordenada do sono. Ainda assim, o mercado atual foca-se principalmente em medições da frequência cardíaca e carece robustez para aplicações clínicas da medição de SpO2. Para além disso, o obstáculo mais comum em medições com PPG é a qualidade do sinal. Consequentemente, este trabalho procura melhorar a robustez clínica destes dispositivos analisando a qualidade do sinal e, posteriormente, extrair métricas relevantes. Primeiramente, foi desenvolvido um protocolo para aquisição de dados de atividades do dia a dia. Assim, foram adquiridos dados com diferentes qualidades, que foram avaliados manualmente de forma a servir de base para os vários modelos de Machine Learning. Também foi desenvolvido um segundo protocolo para a extração do valor de SpO2. Diferentes modelos de Machine Learning foram desenvolvidos para avaliar em tempo real a qualidade do sinal em três qualidades (corrompido, subótimo, ótimo) . Um classificador baseado em Random Forest atingiu exatidões de 79% e 80% em classificadores binários capazes de distinguir entre sinais úteis e inúteis, e exatidões de 74% e 80% a diferenciar entre um sinal subótimo e ótimo, para os dois canais usados. Os classificadores multi-classe atingiram exatidões de 66% e 65% para os dois canais usados. Três medidas clinicamente relevantes foram também extraídas do sinal de PPG: frequências cardíaca e respiratória, cujos algoritmos atingiram resultados semelhantes aos encontrados na literatura e em aparelhos no mercado, e SpO2 que, ainda que promissores, mais dados seriam necessários para os resultados serem estatisticamente significativo

The Influence of Timing on the Hemodynamic Effects of Compression Devices and Development of Sensor Driven Timing Mechanisms

Long periods of reduced mobility are associated with formation of blood clots in the deep veins of the legs, referred to as deep vein thrombosis (DVT). DVT has been noted as a large factor of morbidity and mortality in clinical settings as the clots can move from the legs to the lungs causing blockages, known as pulmonary embolisms. Preventing venous stasis has been clinically linked to preventing DVT formation. Venous stasis can be prevented by applying active mechanical compressions to the lower limbs, such as with an intermittent pneumatic compression (IPC) device; these devices have been clinically shown to reduce venous stasis and thereby prevent DVT formation. The main objectives of this thesis were to assess and improve a custom cardiac gated compression (CGC) system that times compressions based on an individual’s heartrate so as to only apply compressions during the diastolic phase of the cardiac cycle. A comparative performance assessment was done by measuring the central and peripheral hemodynamic changes induced by the use of different IPC devices (ArjoHuntleigh Flowtron ACS800 (Flowtron), Kendall SCD Express (SCD), Aircast Venaflow Elite (VenaFlow), and the custom CGC system) on 12 healthy human subjects in both seated and supine positions. The four selected compression devices had similar applied pressure levels, but dramatically different application profiles with regards to timing and method of application (e.g., single uniform bladder versus sequential inflation from ankle to knee). In addition, the compression systems were delineated by “smart” versus fixed timing, with two devices employing physiological measures to adjust when compression occurred (SCD: slow inflation based on vascular refill time; CGC: rapid inflation based on cardiac gating), and two inflating at fixed intervals (VenaFlow: rapid inflation; Flowtron: slow inflation). The devices were tested for ten minutes while heart rate, stroke volume, cardiac output, calf muscle oxygenation, and femoral venous and superficial femoral arterial velocities measures were collected. From the femoral venous velocities, the velocity per minute, peak velocity during the compression period, and the displacement per compression and per hour were used as performance metrics. With respect to the peak velocity the VenaFlow resulted in higher results than all other devices; likely due to its rapid inflation characteristics and the frequency of compressions allowing pooling to occur in the legs. However, in the performance metric of average displacement per compression the Flowtron and SCD resulted in the greatest displacements; likely because of the devices’ longer inflation periods resulting in a longer increase in venous velocity per compression. However, this measure does not account for the frequency of compressions unlike the displacement per hour measurement; the displacement of venous blood per hour resulted in the CGC device performing as well as the slow inflation devices during supine and resulted in greater displacement than all other devices in the seated position. The CGC also resulted in a sustained increase on the systemic and peripheral hemodynamics in the measures of SV when seated, and arterial velocity and muscle oxygenation when seated and supine; this can potentially be attributed to the device’s cardiac gating and resultant compression frequency. Interestedly, the Flowtron and SCD’s behaviour in most measures taken at steady state were not significantly different despite the “smart” timing employed by the SCD. The current timing mechanism of the CGC device is based on the previous R-R interval of the electrocardiograph (ECG) trace and a fixed pulse wave transit time. Due to heart rate variability and changes in vascular conductance, the compressions of the CGC could be more reliably triggered based on a local measurement of the pulse arrival. To address this need, a custom pulse sensor that uses photoplethysmography (PPG) to detect the pulse in the lower limb was developed. Key problems that impact wearable PPG sensor performance are motion, the power requirements of the LEDs and the ability to work on darker skin pigmentations. The sensor design was done with the primary objective of reliably and robustly detecting the pulse wave arrival regardless of skin tone or application location, and the secondary objective of maximizing the battery life of the sensor for future potential applications in wearable technologies. The developed sensor is reflectance-based and employs 6 independently controlled light emitting diodes (LEDs) surrounding a single photodiode. The independent control enables the “best” LED configuration to be selected through an in situ calibration cycle that results in the strongest signal at the lowest power setting required for different skin pigmentations in non-motion and motion conditions. The shin, in comparison to the foot and ankle, was determined as the best measurement location in terms of motion resistance and low power requirements. Furthermore, the calibration cycle proved to effectively adapt to the underlying physiology and find the LED configuration that resulted in the strongest pulse signal at the lowest possible power setting for each individual. Therefore, as the PPG sensor was proven to work effectively from the lower limb, further improvement to the timing of compressions for the CGC device may be possible through the integration of the peripheral pulse detection sensor.

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

Sensor de fotopletismografia por reflexão sem fios: projeto e desenvolvimento de hardware

Nos anos oitenta do último século começaram a surguir oxímetros wearable que se estabeleceram como um standart para a monitorização da saturação de oxigénio no sangue e actividade cardíaca, de forma não intrusiva. Os referidos oxímetros medem a percentagem de hemoglobina totalmente saturada com oxigénio (SPO2), transmitindo luz com comprimentos de onda diferentes, vermelha e infra-vermelha, através dos tecidos. Os dispositivos wearable atuais são frequentemente desenhados de forma modular, em que o módulo de medição e de display são integrados num único dispositivo. O armazenamento e tratamento de dados é difícil uma vez que são dispositivos de tamanho reduzido; baixo consumo de energia; baixo custo e baixa capacidade de processamento de dados. Tendo em conta que a quantidade de dados recolhidos é relativamente baixa, a sua transmissão de forma wireless é conveniente. Nesta dissertação é desenvolvido e testado um oxímetro de pulso em modo refletivo capaz de cálcular a saturação de oxigénio no sangue, o batimento cardíaco e enviar os dados de forma wireless para outros dispositivos. O hardware desenvolvido engloba quatro módulos funcionais: fonte de alimentação constituída por um conversor DC-DC e um regulador de tensão linear, circuito de carga e monitorização da bateria que controla os ciclos de carga e descarga da bateria, um módulo de rádio frequência que permite que o oxímetro comunique com outros dispositivos de forma wireless e um microcontrolador responsável por gerir todas as comunicações e pelo processamento de sinal. O sofware desenvolvido divide-se em duas partes: uma interface gráfica escrita em Matlab que permite a comunicação entre o computador e o oxímetro e o firmware do microcontrolador que engloba todos os algoritmos de cálculo do SPO2, do batimento cardíaco, drivers de periféricos, gestão das comunicações e aquisição e processamento dos dados.Ever since the early 80s from the last century, wearable oximeters appear as the established standard for non-invasive monitoring of arterial oxygen saturation (SpO2) and heart activity wearable oximeters can monitor arterial SpO2, which is the percentage of arterial hemoglobin that is fully saturated with oxygen, by transmitting red and infrared light through the finger, where it is sensed. The current wearable oximeters are frequently designed as single modular devices, namely, the measurement and display modules are integrated on a single device, which are responsible for several problems. Such devices lack effective data management functions and by being limited by size, power consumption and cost, advanced operating systems cannot be embedded to such wearable oximeters, making difficult to store and manage data. Bearing in mind that the amount of data pulse wave signal collected is small, transmit it wirelessly is convenient and effective. In this thesis a reflective pulse oximeter is developed and tested capable of assessing the oxygen blood saturation (SpO2), the heart rate and send the acquired data through wireless communication to other devices. The developed hardware comprises four functional modules: the power supply made of a DCDC converter and a linear voltage regulator, the charging circuit and battery monitoring system which controls the charging and discharging cycles of the battery, a radio-frequency module that allows the device to connect through wireless communication to other devices and a microcontroller responsible for the management of the communications and for the signal processing. The software developed in this thesis is made of two parts. One being the Matlab graphical interface that allows the communication between the oximeter and the PC while the other one being the microcontroller which comprises all the algorithms of SpO2, heart rate, management of the communication, drivers, and data acquisition and processing

Wearable and Nearable Biosensors and Systems for Healthcare

Biosensors and systems in the form of wearables and “nearables” (i.e., everyday sensorized objects with transmitting capabilities such as smartphones) are rapidly evolving for use in healthcare. Unlike conventional approaches, these technologies can enable seamless or on-demand physiological monitoring, anytime and anywhere. Such monitoring can help transform healthcare from the current reactive, one-size-fits-all, hospital-centered approach into a future proactive, personalized, decentralized structure. Wearable and nearable biosensors and systems have been made possible through integrated innovations in sensor design, electronics, data transmission, power management, and signal processing. Although much progress has been made in this field, many open challenges for the scientific community remain, especially for those applications requiring high accuracy. This book contains the 12 papers that constituted a recent Special Issue of Sensors sharing the same title. The aim of the initiative was to provide a collection of state-of-the-art investigations on wearables and nearables, in order to stimulate technological advances and the use of the technology to benefit healthcare. The topics covered by the book offer both depth and breadth pertaining to wearable and nearable technology. They include new biosensors and data transmission techniques, studies on accelerometers, signal processing, and cardiovascular monitoring, clinical applications, and validation of commercial devices

In vivo investigations of photoplethysmograms and arterial oxygen saturation from the auditory canal in conditions of compromised peripheral perfusion

Pulse oximeters rely on the technique of photoplethysmography (PPG) to estimate arterial oxygen saturation (SpO2). In conditions of poor peripheral perfusion such as hypotension, hypothermia, and vasoconstriction, the PPG signals detected are often small and noisy, or in some cases unobtainable. Hence, pulse oximeters produce erroneous SpO2 readings in these circumstances. The problem arises as most commercial pulse oximeter probes are designed to be attached to peripheral sites such as the finger or toes, which are easily affected by vasoconstriction. In order to overcome this problem, the ear canal was investigated as an alternative site for measuring reliable SpO2 on the hypothesis that blood flow to this central site is preferentially preserved. Novel miniature ear canal PPG sensors were developed along with a state of the art PPG processing unit and a data acquisition system to allow for PPG measurements from different depths and surfaces of the ear canal. A preliminary in vivo investigation on seven healthy volunteers has revealed that good quality PPG signals with high amplitude can be obtained from the posterior surface of the outer ear canal. Based on these observations, a second prototype probe suitable for acquisition of PPGs from the posterior surface of the outer ear canal was developed. A pilot study was then carried out on 15 healthy volunteers to validate the feasibility of measuring PPGs and SpO2 from the ear canal in conditions of induced local peripheral vasoconstriction (right hand immersion in ice water). The PPG signals acquired from the ear canal probe were compared with those obtained simultaneously from finger probes attached to the left and the right index fingers. Significant drop (p 45%) and right (> 50%) index fingers during the ice water immersion, while good quality PPG signals with relatively constant amplitude were obtained from the ear canal. Also, the SpO2 values showed that the ear canal pulse oximeter performed better than the two finger pulse oximeters (mean failure rate 30%). A second in vivo investigation was carried out in 15 healthy volunteers, where hypoperfusion was induced more naturally by exposing the volunteer to cold temperatures of 10C for 10min. Normalised Pulse Amplitude (NPA) and SpO2 was calculated from the PPG signals acquired from the ear canal, the finger and the earlobe. By the end of the cold exposure, a mean drop of > 80% was found in the NPA of finger PPGs. The % drop in the NPA of red and infrared earlobe PPG signals was 20% and 26% respectively. Contrarily to both these sites, the NPA of the ear canal PPGs had only dropped by 0.2% and 13% respectively. The SpO2 estimated from the finger sensor was below 90% in 5 volunteers (failure) by the end of the cold exposure. The earlobe pulse oximeter failed in 3 volunteers. The ear canal sensor on the other hand had only failed in 1 volunteer. These results strongly suggest that the ear canal may be used as a suitable alternative site for reliable monitoring of PPGs and SpO2 in cases of compromised peripheral perfusion