Unobtrusive Monitoring of Heart Rate and Respiration Rate during Sleep

Sleep deprivation has various adverse psychological and physiological effects. The effects range from decreased vigilance causing an increased risk of e.g. traffic accidents to a decreased immune response causing an increased risk of falling ill. Prevalence of the most common sleep disorder, insomnia can be, depending on the study, as high as 30 % in adult population. Physiological information measured unobtrusively during sleep can be used to assess the quantity and the quality of sleep by detecting sleeping patterns and possible sleep disorders. The parameters derived from the signals measured with unobtrusive sensors may include all or some of the following: heartbeat intervals, respiration cycle lengths, and movements. The information can be used in wellness applications that include self-monitoring of the sleep quality or it can also be used for the screening of sleep disorders and in following-up of the effect of a medical treatment. Unobtrusive sensors do not cause excessive discomfort or inconvenience to the user and are thus suitable for long-term monitoring. Even though the monitoring itself does not solve the sleeping problems, it can encourage the users to pay more attention on their sleep. While unobtrusive sensors are convenient to use, their common drawback is that the quality of the signals they produce is not as good as with conventional measurement methods. Movement artifacts, for example, can make the detection of the heartbeat intervals and respiration impossible. The accuracy and the availability of the physiological information extracted from the signals however depend on the measurement principle and the signal analysis methods used. Three different measurement systems were constructed in the studies included in the thesis and signal processing methods were developed for detecting heartbeat intervals and respiration cycle lengths from the measured signals. The performance of the measurement systems and the signal analysis methods were evaluated separately for each system with healthy young adult subjects. The detection of physiological information with the three systems was based on the measurement of ballistocardiographic and respiration movement signals with force sensors placed under the bedposts, the measurement of electrocardiographic (ECG) signal with textile electrodes attached to the bed sheet, and the measurement of the ECG signal with non-contact capacitive electrodes. Combining the information produced by different measurement methods for improving the detection performance was also tested. From the evaluated methods, the most accurate heartbeat interval information was obtained with contact electrodes attached to the bed sheet. The same method also provided the highest heart rate detection coverage. This monitoring method, however, has a limitation that it requires a naked upper body, which is not necessarily acceptable for everyone. For respiration cycle length detection, better results were achieved by using signals recorded with force sensors placed under a bedpost than when extracting the respiration information from the ECG signal recorded with textile bed sheet electrodes. From the data quality point of view, an ideal night-time physiological monitoring system would include a contact ECG measurement for the heart rate monitoring and force sensors for the respiration monitoring. The force sensor signals could also be used for movement detection

Fusion enhancement for tracking of respiratory rate through intrinsic mode functions in photoplethysmography

Decline in respiratory regulation demonstrates the primary forewarning for the onset of physiological aberrations. In clinical environment, the obtrusive nature and cost of instrumentation have retarded the integration of continuous respiration monitoring for standard practice. Photoplethysmography (PPG) presents a non-invasive, optical method of assessing blood flow dynamics in peripheral vasculature. Incidentally, respiration couples as a surrogate constituent in PPG signal, justifying respiratory rate (RR) estimation. The physiological processes of respiration emerge as distinctive oscillations that are fluctuations in various parameters extracted from PPG signal. We propose a novel algorithm designed to account for intermittent diminishment of the respiration induced variabilities (RIV) by a fusion-based enhancement of wavelet synchrosqueezed spectra. We have combined the information on intrinsic mode functions (IMF) of five RIVs to enhance mutually occurring, instantaneous frequencies of the spectra. The respiration rate estimate is obtained by tracking the spectral ridges with a particle filter. We have evaluated the method with a dataset recorded from 29 young adult subjects (mean: 24.17 y, SD: 4.19 y) containing diverse, voluntary, and periodically metronome-assisted respiratory patterns. Bayesian inference on fusion-enhanced Respiration Induced Frequency Variability (RIFV) indicated MAE and RMSE of 1.764 and 3.996 BPM, respectively. The fusion approach was deemed to improve MAE and RMSE of RIFV by 0.185 BPM (95% HDI: 0.0285-0.3488, effect size: 0.548) and 0.250 BPM (95% HDI: 0.0733-0.431, effect size: 0.653), respectively, with further pronounced improvements to other RIVs. We conclude that the fusion of variability signals proves important to IMF localization in the spectral estimation of RR.acceptedVersionPeer reviewe

IRlab - Platform for thermal video analysis in evaluation of peripheral thermal behavior and blood perfusion

Background and objectives: Dynamic thermal imaging in medicine has several advantages in comparison to static thermal image analysis and has potential as a novel patient assessment method e.g. in the area of vascular surgery. Since dynamic thermal imaging has become in the scope of research only during the last decade, the computational available analysis methods are often lacking or not existing. Most of the published software is not available to the research community or are behind a paywall. IRlab provides an easy-to-use dynamic thermal video processing and analysis platform, freely accessible to researchers. Methods: IRlab is programmed in Matlab R2020b. Computational tools for dynamic analysis are divided into spatio-temporal and spectral methods, where spatio-temporal methods consist of region of interest delineation tools, thermal modulation analysis, standard thermal measures such as median, maximum, minimum and deviation values, and subtraction and gamma maps. Spectral methods include spectral band power, spectral flow, and wavelet analysis tools. Preliminary data of a single healthy subject was analyzed with the program as a sample run. Results: IRlab provides a platform for lower limb thermal image and video analysis with a clear workflow and variety of processing and analysis tools for time and frequency space analysis. The whole source code for IRlab is freely available for the research community under the General public license. Conclusions: IRlab is a versatile tool for dynamic thermal image and video processing. Freeware and open-source programs for medical thermal imaging are severely lacking, thus as a completely open-source project IRlab offers a unique platform for researchers within the field of medical thermal imaging.publishedVersionPeer reviewe

Evaluation of a wrist-worn photoplethysmography monitor for heart rate variability estimation in patients recovering from laparoscopic colon resection

To evaluate the accuracy of heart rate variability (HRV) parameters obtained with a wrist-worn photoplethysmography (PPG) monitor in patients recovering from minimally invasive colon resection to investigate whether PPG has potential in postoperative patient monitoring. 31 patients were monitored for three days or until discharge or reoperation using a wrist-worn PPG monitor (PulseOn, Finland) with a Holter monitor (Faros 360, Bittium Biosignals, Finland) as a reference measurement device. Beat-to-beat intervals (BBI) and HRV information collected by PPG were compared with RR intervals (RRI) and HRV obtained from the ECG reference after removing artefacts and ectopic beats. The beat-to-beat mean error (ME) and mean absolute error (MAE) of good quality heartbeat intervals obtained by wrist PPG were estimated as - 1.34 ms and 10.4 ms respectively. A significant variation in the accuracy of the HRV parameters was found. In the time domain, SDNN (9.11%), TRI (11.4%) and TINN (11.1%) were estimated with low relative MAE, while RMSSD (34.3%), pNN50 (139%) and NN50 (188%) had higher errors. The logarithmic parameters in the frequency domain (VLF Log, LF Log and HF Log) exhibited the lowest relative error, and for non-linear parameters, SD2 (7.5%), DFA alpha 1 (8.25%) and DFA alpha 2 (4.71%) were calculated much more accurately than SD1 (34.3%). The wrist PPG shows some potential for use in a clinical setting. The accuracy of several HRV parameters analyzed post hoc was found sufficient to be used in further studies concerning postoperative recovery of patients undergoing laparoscopic colon resection, although there were large errors in many common HRV parameters such as RMSSD, pNN50 and NN50, rendering them unusable. ClinicalTrials.gov Identifier: NCT04996511, August 9, 2021, retrospectively registeredPeer reviewe

Arterial pulse waves measured with EMFi and PPG sensors and comparison of the pulse waveform spectral and decomposition analysis in healthy subjects

The purpose of this study is to show the time domain and frequency domain analysis of signals recorded with Electromechanical Film (EMFi) and Photoplethysmographic (PPG) sensors in arterial elasticity estimation via pulse wave decomposition and spectral components obtained from left forefinger, wrist, and second toe arteries. ECG and pulse waves from the subjects were recorded from 7 persons (30‐60 y) in supine position. Decomposition of the pulse waves produces five components: percussion, tidal, dicrotic, repercussion, and retidal waves. Pulse wave decomposition parameters between EMFi and PPG are compared to detect variables for information on person’s arterial elasticity. Results show that elasticity information in the form of pulse wave decomposition from PPG and EMFi waves is obtainable and shows clear shortening between percussion wave and tidal wave peak time in PPG waveforms with age. The spectral information obtained with frequency domain analysis could also be valuable in assessment of the arterial elasticity. In addition, both PPG and EMFi measurements are absolutely non‐invasive and safe. In PPG measurement, the sensors are on the opposite sides of the finger tip, however, EMFi measurement needs the good skilled operator attaching the sensor on the patient’s wrist by touching gently to obtain accurate waveforms

Parkinson’s disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study

Parkinson’s disease (PD) is a neurodegenerative disease inducing dystrophy of the motor system. Automatic movement analysis systems have potential in improving patient care by enabling personalized and more accurate adjust of treatment. These systems utilize machine learning to classify the movement properties based on the features derived from the signals. Smartphones can provide an inexpensive measurement platform with their built-in sensors for movement assessment. This study compared three feature selection and nine classification methods for identifying PD patients from control subjects based on accelerometer and gyroscope signals measured with a smartphone during a 20-step walking test. Minimum Redundancy Maximum Relevance (mRMR) and sequential feature selection with both forward (SFS) and backward (SBS) propagation directions were used in this study. The number of selected features was narrowed down from 201 to 4–15 features by applying SFS and mRMR methods. From the methods compared in this study, the highest accuracy for individual steps was achieved with SFS (7 features) and Naive Bayes classifier (accuracy 75.3%), and the second highest accuracy with SFS (4 features) and k Nearest neighbours (accuracy 75.1%). Leave-one-subject-out cross-validation was used in the analysis. For the overall classification of each subject, which was based on the majority vote of the classified steps, k Nearest Neighbors provided the most accurate result with an accuracy of 84.5% and an error rate of 15.5%. This study shows the differences in feature selection methods and classifiers and provides generalizations for optimizing methodologies for smartphone-based monitoring of PD patients. The results are promising for further developing the analysis system for longer measurements carried out in free-living conditions.Peer reviewe

The use of wrist EMG increases the PPG Heart Rate accuracy in smartwatches

The impact of tissue movements on the accuracy of heart rate (HR) estimates is a challenge in today’s wearable technology. Tissue movements are caused by muscle activity that modifies the optical path of the reflectance photoplethysmography (PPG), leading to motion artifacts (MA) that mask the true HR. This kind of MA is not always detected using accelerometers (ACC). In this study, we propose a method to increase the PPG HR accuracy of a wristwatch using wrist surface electromyogram (EMG) and ACC using spectrum subtraction algorithms. We collected the wrist EMG, wristwatch PPG, ACC data, and the ECG from nine subjects. Data were recorded during four frequent hand movements and three activities (weightlifting and running/walking with and without holding a racket). The added value of the EMG was studied. Visual results indicate that wrist EMG correlates well with the MA seen in the PPG signal and provides additional information over the typically used ACC data. Our analysis showed that the proposed artifact removal method using EMG and ACC decreases the HR estimation error on average by 49% compared to only ACC. Our results showed that wrist EMG contains complementary information on the PPG artifacts and offers a novel signal modality for improving optical HR estimation accuracy in smartwatches.acceptedVersionPeer reviewe

Assessment of chronic limb threatening ischemia using thermal imaging

Objectives: Current chronic limb threatening ischemia (CLTI) diagnostics require expensive equipment, using ionizing radiation or contrast agents, or summative surrogate methods lacking in spatial information. Our aim is to develop and improve contactless, non-ionizing and cost-effective diagnostic methods for CLTI assessment with high spatial accuracy by utilizing dynamic thermal imaging and the angiosome concept. Approach: Dynamic thermal imaging test protocol was suggested and implemented with a number of computational parameters. Pilot data was measured from 3 healthy young subjects, 4 peripheral artery disease (PAD) patients and 4 CLTI patients. The protocol consists of clinical reference measurements, including ankle- and toe-brachial indices (ABI, TBI), and a modified patient bed for hydrostatic and thermal modulation tests. The data was analyzed using bivariate correlation. Results: The thermal recovery time constant was on average higher for the PAD (88%) and CLTI (83%) groups with respect to the healthy young subjects. The contralateral symmetry was high for the healthy young group and low for the CLTI group. The recovery time constants showed high negative correlation to TBI (ρ = -0.73) and ABI (ρ = -0.60). The relation of these clinical parameters to the hydrostatic response and absolute temperatures (|ρ|<0.3) remained unclear. Conclusion: The lack of correlation for absolute temperatures or their contralateral differences with the clinical status, ABI and TBI disputes their use in CLTI diagnostics. Thermal modulation tests tend to augment the signs of thermoregulation deficiencies and accordingly high correlations were found with all reference metrics. The method is promising for establishing the connection between impaired perfusion and thermography. The hydrostatic modulation test requires more research with stricter test conditions.publishedVersionPeer reviewe

Comparison of Electrode Configurations for Impedance Plethysmography Based Heart Rate Estimation at the Forearm

Electrical impedance plethysmography (EIP) is a cost effective and power efficient physiological measurement method that could potentially be applied for measuring pulse waves along limbs in ambulatory conditions. The pulse wave information could be utilized to determine the heart rate or other relevant parameters such as heart rate variability or cardiac rhythm. We compared three electrode configurations for EIP at the forearm, with the focus on assessing its utility in a wearable device. The evaluation included EIP measurements with ten healthy participants using adhesive gel electrodes. The evaluated electrode configurations were tetrapolar configuration along the forearm and tetrapolar and bipolar configurations around the wrist. For each electrode configuration, the measurements were performed in stationery condition and during finger movement. The collected data was evaluated for finding out differences in the signal to noise ratio (SNR) between the configurations during the two conditions. The results show that pulse wave signal with adequate SNR for heart rate estimation is obtained from the wrist area while stationary and mostly also during the presence of mild movement. There was no significant difference in the data quality between wrist area and conventional configuration along the limb.acceptedVersionPeer reviewe

Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KAVELI) : Protocol for an Observational Case-Control Study

Background: Clinical characterization of motion in patients with Parkinson disease (PD) is challenging: symptom progression, suitability of medication, and level of independence in the home environment can vary across time and patients. Appointments at the neurological outpatient clinic provide a limited understanding of the overall situation. In order to follow up these variations, longer-term measurements performed outside of the clinic setting could help optimize and personalize therapies. Several wearable sensors have been used to estimate the severity of symptoms in PD; however, longitudinal recordings, even for a short duration of a few days, are rare. Home recordings have the potential benefit of providing a more thorough and objective follow-up of the disease while providing more information about the possible need to change medications or consider invasive treatments. Objective: The primary objective of this study is to collect a dataset for developing methods to detect PD-related symptoms that are visible in walking patterns at home. The movement data are collected continuously and remotely at home during the normal lives of patients with PD as well as controls. The secondary objective is to use the dataset to study whether the registered medication intakes can be identified from the collected movement data by looking for and analyzing short-term changes in walking patterns. Methods: This paper described the protocol for an observational case-control study that measures activity using three different devices: (1) a smartphone with a built-in accelerometer, gyroscope, and phone orientation sensor, (2) a Movesense smart sensor to measure movement data from the wrist, and (3) a Forciot smart insole to measure the forces applied on the feet. The measurements are first collected during the appointment at the clinic conducted by a trained clinical physiotherapist. Subsequently, the subjects wear the smartphone at home for 3 consecutive days. Wrist and insole sensors are not used in the home recordings. Results: Data collection began in March 2018. Subject recruitment and data collection will continue in spring 2019. The intended sample size was 150 subjects. In 2018, we collected a sample of 103 subjects, 66 of whom were diagnosed with PD. Conclusions: This study aims to produce an extensive movement-sensor dataset recorded from patients with PD in various phases of the disease as well as from a group of control subjects for effective and impactful comparison studies. The study also aims to develop data analysis methods to monitor PD symptoms and the effects of medication intake during normal life and outside of the clinic setting. Further applications of these methods may include using them as tools for health care professionals to monitor PD remotely and applying them to other movement disorders.Peer reviewe