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

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

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

Recent development of respiratory rate measurement technologies

Respiratory rate (RR) is an important physiological parameter whose abnormity has been regarded as an important indicator of serious illness. In order to make RR monitoring simple to do, reliable and accurate, many different methods have been proposed for such automatic monitoring. According to the theory of respiratory rate extraction, methods are categorized into three modalities: extracting RR from other physiological signals, RR measurement based on respiratory movements, and RR measurement based on airflow. The merits and limitations of each method are highlighted and discussed. In addition, current works are summarized to suggest key directions for the development of future RR monitoring methodologies

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

Movement Sensor Dataset for Dog Behavior Classification

The dataset includes movement sensor data from sensors placed on the collar and the harness of a dog and recorded while the dog is given tasks or activities to perform. The task are: galloping, lying on chest, sitting, sniffing, standing, trotting, and walking. The movement sensors used are: ActiGraph GT9X Link (ActiGraph LLC, Florida, USA) and they include 3D accelerometer and 3D gyroscope. The sampling rate used is 100 Hz. The dataset is described in more detail in the data description article: Vehkaoja, A., Somppi, S., Törnqvist, H., Valldeoriola Cardó, A., Kumpulainen, P., Väätäjä, H., Majaranta, P., Surakka, V., Kujala, M. V., Vainio, O., Description of Movement Sensor Dataset for Dog Behavior Classification, Data in Brief, 2022.The behavior classification results obtained with the dataset are published in: Kumpulainen, P., Valldeoriola Cardó, A., Somppi, S., Törnqvist, H., Väätäjä, H., Majaranta, P., Gizatdinova, Y., Hoog Antink, C., Surakka, V., Kujala, M. V., Vainio, O., and Vehkaoja, A., Dog behaviour classification with movement sensors placed on the harness and the collar, Applied Animal Behavior Science, 241 (2021): 105393. https://doi.org/10.1016/j.applanim.2021.105393The authors of the dataset request researchers to refer to the aforementioned publications when using the data and publishing results produced using it.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Movement Sensor Dataset for Dog Behavior Classification

The dataset includes movement sensor data from sensors placed on the collar and the harness of a dog and recorded while the dog is given tasks or activities to perform. The task are: galloping, lying on chest, sitting, sniffing, standing, trotting, and walking. The movement sensors used are: ActiGraph GT9X Link (ActiGraph LLC, Florida, USA) and they include 3D accelerometer and 3D gyroscope. The sampling rate used is 100 Hz. The dataset is described in more detail in the data description article: Vehkaoja, A., Somppi, S., Törnqvist, H., Valldeoriola Cardó, A., Kumpulainen, P., Väätäjä, H., Majaranta, P., Surakka, V., Kujala, M. V., Vainio, O., Description of Movement Sensor Dataset for Dog Behavior Classification, Data in Brief, 2021. The behavior classification results obtained with the dataset are published in: Kumpulainen, P., Valldeoriola Cardó, A., Somppi, S., Törnqvist, H., Väätäjä, H., Majaranta, P., Gizatdinova, Y., Hoog Antink, C., Surakka, V., Kujala, M. V., Vainio, O., and Vehkaoja, A., Dog behaviour classification with movement sensors placed on the harness and the collar, Applied Animal Behavior Science, 2021. The authors of the dataset request researchers to refer to the aforementioned publications when using the data and publishing results produced using it

Movement Sensor Dataset for Dog Behavior Classification

The dataset includes movement sensor data from sensors placed on the collar and the harness of a dog and recorded while the dog is given tasks or activities to perform. The task are: galloping, lying on chest, sitting, sniffing, standing, trotting, and walking. The movement sensors used are: ActiGraph GT9X Link (ActiGraph LLC, Florida, USA) and they include 3D accelerometer and 3D gyroscope. The sampling rate used is 100 Hz. The dataset is described in more detail in the data description article: Vehkaoja, A., Somppi, S., Törnqvist, H., Valldeoriola Cardó, A., Kumpulainen, P., Väätäjä, H., Majaranta, P., Surakka, V., Kujala, M. V., Vainio, O., Description of Movement Sensor Dataset for Dog Behavior Classification, Data in Brief, 2022.The behavior classification results obtained with the dataset are published in: Kumpulainen, P., Valldeoriola Cardó, A., Somppi, S., Törnqvist, H., Väätäjä, H., Majaranta, P., Gizatdinova, Y., Hoog Antink, C., Surakka, V., Kujala, M. V., Vainio, O., and Vehkaoja, A., Dog behaviour classification with movement sensors placed on the harness and the collar, Applied Animal Behavior Science, 241 (2021): 105393. https://doi.org/10.1016/j.applanim.2021.105393The authors of the dataset request researchers to refer to the aforementioned publications when using the data and publishing results produced using it.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Auto-regression-driven, reallocative particle filtering approaches in PPG-based respiration rate estimation

Interest towards respiratory state assessment with non-obtrusive instrumentation has led to the design of novel algorithmic solutions. Notably, respiratory behavior has been observed to cause modulative changes in two discreetly measurable physiological signals, PPG and ECG. The potential to integrate respiratory rate measurements in widely used instrumentation with no additional cost has made the research of suitable signal processing methods attractive. We have studied and compared auto-regressive (AR) model order optimization and coefficient extraction methods combined with a reallocative particle filtering approach for respiration rate estimation from finger PPG signal. The evaluated coefficient extraction methods were Yule-Walker, Burg, and Least-square. Considered model order optimization methods were Akaike’s information criteria (AIC) and Minimum description length. Methods were evaluated with a publicly available dataset comprised of approximately 10-minute measurements from 39 healthy subjects at rest. From the evaluated AR model parameter extraction methods, Burg's method combined AIC performed the best. We obtained the mean absolute error of 2.7 and bias of -0.4 respirations per minute with this combination.acceptedVersionPeer reviewe