Archives: February 2017

Back

Stress management is essential in this modern civilization to maintain one's stress level low and reduce health risks, since stress is one of the primary causes leading to major chronic health disorders. The present study investigates the validity of stress index (SI) metric that objectively quantifies the psychological acute stress using a disposable adhesive biosensor worn on the chest called as HealthPatch®. Eleven healthy volunteers (n=11) were attached with one HealthPatch sensor at left pectoralis major muscle along the cardiac axis to record modified Lead-II ECG. The subjects carried out a standard Trier Social Stress Test (TSST) protocol. During the study, the subjects filled out state anxiety form-Y1 of the State Anxiety Inventory questionnaire (sSTAI); salivary samples were obtained for salivary alpha-amylase (sAA) and salivary cortisol (sC) measurements; and the HealthPatch sensor data were wirelessly acquired. The data analyses revealed that sSTAI scores were significantly increased (P<;0.001) due to TSST compared to the baseline. But, the changes in both sAA and sC measurements were not significant (P=0.281 and P=0.792, respectively). On the other hand, SI metric of HealthPatch showed significant (P<;0.001) increase (~50%) during TSST, and shown to be sensitive to objectively track acute changes in psychological stress. Thus, HealthPatch biosensor can be valuable for continuous monitoring of psychological health and effective management of stress leading to healthy life.

Psychological Acute Stress Measurement Using a Wireless Adhesive Biosensor

Stress management is essential in this modern civilization to maintain one's stress level low and reduce health risks, since stress is one of the primary causes leading to major chronic health disorders. The present study investigates the validity of stress index (SI) metric that objectively quantifies the psychological acute stress using a disposable adhesive biosensor worn on the chest called as HealthPatch®. Eleven healthy volunteers (n=11) were attached with one HealthPatch sensor at left pectoralis major muscle along the cardiac axis to record modified Lead-II ECG. The subjects carried out a standard Trier Social Stress Test (TSST) protocol. During the study, the subjects filled out state anxiety form-Y1 of the State Anxiety Inventory questionnaire (sSTAI); salivary samples were obtained for salivary alpha-amylase (sAA) and salivary cortisol (sC) measurements; and the HealthPatch sensor data were wirelessly acquired. The data analyses revealed that sSTAI scores were significantly increased (P<;0.001) due to TSST compared to the baseline. But, the changes in both sAA and sC measurements were not significant (P=0.281 and P=0.792, respectively). On the other hand, SI metric of HealthPatch showed significant (P<;0.001) increase (~50%) during TSST, and shown to be sensitive to objectively track acute changes in psychological stress. Thus, HealthPatch biosensor can be valuable for continuous monitoring of psychological health and effective management of stress leading to healthy life.

Back

Traditional systems for energy expenditure (EE) assessment are impractical for continuous monitoring in free-living conditions. The study presents the performance of a chest-worn wireless HealthPatch sensor for the continuous estimation of EE rate and total energy expenditure (TEE) based on the heart rate and acceleration signals of upper torso. Volunteers (n=32) were attached with patch sensors at three locations on chest, a portable metabolic analyzer, three commercial devices: BodyMediaFIT, Nike+FuelBand and FitBitForce for comparative analysis. Participants carried out a protocol consisted of resting, mild, moderate and intense level of exercises that lasted for 90 min. Analyses of correlation, performance errors and agreement were carried out for the EE rate and TEE values of the patch sensor compared to the metabolic analyzer. The correlation coefficient and mean absolute error of patch sensor’s EE rate were 0.94+0.04 and 0.67+0.24 (Kcal/min), respectively for the collective three patch locations. The patch sensor offered the most accurate estimates of TEE with least mean absolute percentage error of <15%, least bias (0.8 Kcal) and narrowest 95% limits of agreement (-79 to 81 Kcal) than the other consumer based wearable sensors...

Performance of Energy Expenditure Assessment Using a Chest-worn Wireless Patch Sensor

Traditional systems for energy expenditure (EE) assessment are impractical for continuous monitoring in free-living conditions. The study presents the performance of a chest-worn wireless HealthPatch sensor for the continuous estimation of EE rate and total energy expenditure (TEE) based on the heart rate and acceleration signals of upper torso. Volunteers (n=32) were attached with patch sensors at three locations on chest, a portable metabolic analyzer, three commercial devices: BodyMediaFIT, Nike+FuelBand and FitBitForce for comparative analysis. Participants carried out a protocol consisted of resting, mild, moderate and intense level of exercises that lasted for 90 min. Analyses of correlation, performance errors and agreement were carried out for the EE rate and TEE values of the patch sensor compared to the metabolic analyzer. The correlation coefficient and mean absolute error of patch sensor’s EE rate were 0.94+0.04 and 0.67+0.24 (Kcal/min), respectively for the collective three patch locations. The patch sensor offered the most accurate estimates of TEE with least mean absolute percentage error of <15%, least bias (0.8 Kcal) and narrowest 95% limits of agreement (-79 to 81 Kcal) than the other consumer based wearable sensors...

Back

Continuous monitoring of respiratory rate in am- bulatory conditions has widespread applications for screening of respiratory diseases and remote patient monitoring. Unfortunately, minimally obtrusive techniques often suffer from low accuracy. In this paper, we describe an algorithm with low computational complexity for combining multiple respiratory measurements to estimate breathing rate from an unobtrusive chest patch sensor. Respiratory rates derived from the respi- ratory sinus arrhythmia (RSA) and modulation of the QRS amplitude of electrocardiography (ECG) are combined with a respiratory rate derived from tri-axial accelerometer data. The three respiration rates are combined by a weighted average using weights based on quality metrics for each signal. The algorithm was evaluated on 15 elderly subjects who performed spontaneous and metronome breathing as well as a variety of activities of daily living (ADLs). When compared to a reference device, the mean absolute error was 1.02 breaths per minute (BrPM) during metronome breathing, 1.67 BrPM during spontaneous breathing, and 2.03 BrPM during ADLs....

Ambulatory Respiratory Rate Detection using ECG and a Triaxial Accelerometer

Continuous monitoring of respiratory rate in am- bulatory conditions has widespread applications for screening of respiratory diseases and remote patient monitoring. Unfortunately, minimally obtrusive techniques often suffer from low accuracy. In this paper, we describe an algorithm with low computational complexity for combining multiple respiratory measurements to estimate breathing rate from an unobtrusive chest patch sensor. Respiratory rates derived from the respi- ratory sinus arrhythmia (RSA) and modulation of the QRS amplitude of electrocardiography (ECG) are combined with a respiratory rate derived from tri-axial accelerometer data. The three respiration rates are combined by a weighted average using weights based on quality metrics for each signal. The algorithm was evaluated on 15 elderly subjects who performed spontaneous and metronome breathing as well as a variety of activities of daily living (ADLs). When compared to a reference device, the mean absolute error was 1.02 breaths per minute (BrPM) during metronome breathing, 1.67 BrPM during spontaneous breathing, and 2.03 BrPM during ADLs....

Back

There has been a growing interest in out-of-center sleep testing with portable devices for accurate diagnosis of sleep apnea syndrome. This paper presents a new algorithm that extracts features based on filtering and statistical dispersion of the nasal airflow respiration signal and detects apnea events on a per-second basis. The data records were randomly selected from the Sleep Heart Health Study (SHHS-2) database to represent 100 control subjects with Apnea-Hypopnea Index (AHI) less than 5, and 100 apnea subjects with AHI values from 30 to 75. The algorithm was optimized according to the product of sensitivity and positive predictive value of apnea events among a training dataset of 50 apnea subjects with a constraint on the false positive rate among a training dataset of 50 control subjects. From testing of the algorithm on separate datasets, the false positive rate among 50 control subjects was found to be 1.3 events per hour, which corresponds to 100% specificity of classifying apnea subjects. The sensitivity and positive predictive value among 50 apnea subjects were found to be 83.6% and 72.3%, respectively. Among the identified false positive events in the apnea subjects, 64.3% of the events were found to be hypopnea events. Thus, incorporation of hypopnea detection would enhance the performance of the apnea detection algorithm...

Detection of Sleep Apnea on a Per-Second Basis Using Respiratory Signals

There has been a growing interest in out-of-center sleep testing with portable devices for accurate diagnosis of sleep apnea syndrome. This paper presents a new algorithm that extracts features based on filtering and statistical dispersion of the nasal airflow respiration signal and detects apnea events on a per-second basis. The data records were randomly selected from the Sleep Heart Health Study (SHHS-2) database to represent 100 control subjects with Apnea-Hypopnea Index (AHI) less than 5, and 100 apnea subjects with AHI values from 30 to 75. The algorithm was optimized according to the product of sensitivity and positive predictive value of apnea events among a training dataset of 50 apnea subjects with a constraint on the false positive rate among a training dataset of 50 control subjects. From testing of the algorithm on separate datasets, the false positive rate among 50 control subjects was found to be 1.3 events per hour, which corresponds to 100% specificity of classifying apnea subjects. The sensitivity and positive predictive value among 50 apnea subjects were found to be 83.6% and 72.3%, respectively. Among the identified false positive events in the apnea subjects, 64.3% of the events were found to be hypopnea events. Thus, incorporation of hypopnea detection would enhance the performance of the apnea detection algorithm...

Back

Unobtrusive continuous monitoring of important vital signs and activity metrics has the potential to provide remote health monitoring, at-home screening, and rapid notification of critical events such as heart attacks, falls, or respiratory distress. This paper contains validation results of a wireless Bluetooth Low Energy (BLE) patch sensor consisting of two electrocardiography (ECG) electrodes, a microcontroller, a tri-axial accelerometer, and a BLE transceiver. The sensor measures heart rate, heart rate variability (HRV), respiratory rate, posture, steps, and falls and was evaluated on a total of 25 adult participants who performed breathing exercises, activities of daily living (ADLs), various stretches, stationary cycling, walking/running, and simulated falls. Compared to reference devices, the heart rate measurement had a mean absolute error (MAE) of less than 2 bpm, time-domain HRV measurements had an RMS error of less than 15 ms, respiratory rate had an MAE of 1.1 breaths per minute during metronome breathing, posture detection had an accuracy of over 95% in two of the three patch locations, steps were counted with an absolute error of less than 5%, and falls were detected with a sensitivity of 95.2% and specificity of 100%...

Wireless Patch Sensor for Remote Monitoring of Heart Rate Respiration, Activity, and Falls

Unobtrusive continuous monitoring of important vital signs and activity metrics has the potential to provide remote health monitoring, at-home screening, and rapid notification of critical events such as heart attacks, falls, or respiratory distress. This paper contains validation results of a wireless Bluetooth Low Energy (BLE) patch sensor consisting of two electrocardiography (ECG) electrodes, a microcontroller, a tri-axial accelerometer, and a BLE transceiver. The sensor measures heart rate, heart rate variability (HRV), respiratory rate, posture, steps, and falls and was evaluated on a total of 25 adult participants who performed breathing exercises, activities of daily living (ADLs), various stretches, stationary cycling, walking/running, and simulated falls. Compared to reference devices, the heart rate measurement had a mean absolute error (MAE) of less than 2 bpm, time-domain HRV measurements had an RMS error of less than 15 ms, respiratory rate had an MAE of 1.1 breaths per minute during metronome breathing, posture detection had an accuracy of over 95% in two of the three patch locations, steps were counted with an absolute error of less than 5%, and falls were detected with a sensitivity of 95.2% and specificity of 100%...

Back

Sleep Apnea Syndrome (SAS) is highly prevalent worldwide and severely affects quality of life. SAS screening with self-reported symptoms, upper-airway features, physical exam and questionnaires have been reported with sensitivity but poor specificity. Challenges with polysomnography (PSG) for SAS screening include high operating costs, inadequate availability and limited repeatability. We present a novel SAS screening tool using the Vital Connect HealthPatch™ sensor.

Wireless Patch Sensor for Screening of Sleep Apnea

Sleep Apnea Syndrome (SAS) is highly prevalent worldwide and severely affects quality of life. SAS screening with self-reported symptoms, upper-airway features, physical exam and questionnaires have been reported with sensitivity but poor specificity. Challenges with polysomnography (PSG) for SAS screening include high operating costs, inadequate availability and limited repeatability. We present a novel SAS screening tool using the Vital Connect HealthPatch™ sensor.

Back

Polysomnography (PSG) is the gold standard that manually quantifies the apnea-hypopnea index (AHI) to assess the severity of sleep apnea syndrome (SAS). This study presents an algorithm that automatically estimates the AHI value using a disposable HealthPatch sensor. Volunteers (n=53, AHI: 0.1?-85.8) participated in an overnight PSG study with patch sensors attached to their chest at three specified locations and data were wirelessly acquired. Features were computed for 150- second epochs of patch sensor data using analyses of heart rate variability, respiratory signals, posture and movements. Linear Support Vector Machine classifier was trained to detect the presence/absence of apnea/hypopnea events for each epoch. The number of epochs identified with events was subsequently mapped to AHI values using quadratic regression analysis. The classifier and regression models were optimized to minimize the mean-square error of AHI based on leave-one-out cross-validation. Comparison of predicted and reference AHI values resulted in linear correlation coefficients of 0.87, 0.88 and 0.92 for the three locations, respectively. The predicted AHI values were subsequently used to classify the control-to-mild apnea group (AHI<15) and moderate-to-severe apnea (AHI>=15) with an accuracy (95% confidence intervals) of 89.4% (77.4?-95.4%), 85.0% (70.9?-92.9%), and 82.9% (67.3?-91.9%) for the three locations, respectively. Overnight physiological monitoring using a wireless patch sensor provides an accurate estimate of AHI...

Automated Prediction of the Apnea-Hypopnea Index using a Wireless Patch Sensor

Polysomnography (PSG) is the gold standard that manually quantifies the apnea-hypopnea index (AHI) to assess the severity of sleep apnea syndrome (SAS). This study presents an algorithm that automatically estimates the AHI value using a disposable HealthPatch sensor. Volunteers (n=53, AHI: 0.1?-85.8) participated in an overnight PSG study with patch sensors attached to their chest at three specified locations and data were wirelessly acquired. Features were computed for 150- second epochs of patch sensor data using analyses of heart rate variability, respiratory signals, posture and movements. Linear Support Vector Machine classifier was trained to detect the presence/absence of apnea/hypopnea events for each epoch. The number of epochs identified with events was subsequently mapped to AHI values using quadratic regression analysis. The classifier and regression models were optimized to minimize the mean-square error of AHI based on leave-one-out cross-validation. Comparison of predicted and reference AHI values resulted in linear correlation coefficients of 0.87, 0.88 and 0.92 for the three locations, respectively. The predicted AHI values were subsequently used to classify the control-to-mild apnea group (AHI<15) and moderate-to-severe apnea (AHI>=15) with an accuracy (95% confidence intervals) of 89.4% (77.4?-95.4%), 85.0% (70.9?-92.9%), and 82.9% (67.3?-91.9%) for the three locations, respectively. Overnight physiological monitoring using a wireless patch sensor provides an accurate estimate of AHI...

Back

Remote monitoring and wearable technologies could help to effectively manage health, monitor safety andreduce the staggering health care costs. The present studyis designed to investigate the efficacy of disposable wireless HealthPatch sensors for continuous and long-term monitoring in senior subjects over consecutive 50-days in their home setting. Patch sensor is worn on the chest that allows remote and real-time monitoring of vital sign measurements and falls. Patch data and nightly questionnaire responses were obtained in 76 participants (age 59-85 years) over 3603 days using 1333 patches, collectively. The performance of heart rate, respiration rate and skin temperature was assessed on day-1 and day-4 that estimated the mean absolute errors to be <3 beats/min, <3 breaths/min and <1.2 C, respectively compared to their respective reference devices. The vital signs showed no significant differences between start and end of a 3-day wear cycle. False positive rate of fall detection was 0.0027 falls/day. The participants reported the patch wear very/fairly comfortable for 88.2% of days of wear. The wireless patch sensors have demonstrated high performance over its 3-day wear cycle, great compliance, and positive user feedback on wearability and usability. Thus, the patch sensors are very efficient and suitable for remote long-term monitoring at uncontrolled home setting...

Long-term Remote Monitoring of Vital Signs using a Wireless Patch Sensor

Remote monitoring and wearable technologies could help to effectively manage health, monitor safety andreduce the staggering health care costs. The present studyis designed to investigate the efficacy of disposable wireless HealthPatch sensors for continuous and long-term monitoring in senior subjects over consecutive 50-days in their home setting. Patch sensor is worn on the chest that allows remote and real-time monitoring of vital sign measurements and falls. Patch data and nightly questionnaire responses were obtained in 76 participants (age 59-85 years) over 3603 days using 1333 patches, collectively. The performance of heart rate, respiration rate and skin temperature was assessed on day-1 and day-4 that estimated the mean absolute errors to be <3 beats/min, <3 breaths/min and <1.2 C, respectively compared to their respective reference devices. The vital signs showed no significant differences between start and end of a 3-day wear cycle. False positive rate of fall detection was 0.0027 falls/day. The participants reported the patch wear very/fairly comfortable for 88.2% of days of wear. The wireless patch sensors have demonstrated high performance over its 3-day wear cycle, great compliance, and positive user feedback on wearability and usability. Thus, the patch sensors are very efficient and suitable for remote long-term monitoring at uncontrolled home setting...