Community-based exercise program, self-reported health-related symptoms, and quality of life in persons with traumatic brain injury 45 + years old.

BACKGROUND: Individuals with moderate to severe traumatic brain injury (msTBI) have reported a lack of motivation, lack of time, and fatigue as perceived barriers to exercise. OBJECTIVE: To evaluate the effects of an exercise program on self-reported health-related symptoms and quality of life in persons 45-years and older with msTBI. METHODS: Post-hoc analysis of a prospective community-based 12-week exercise program of 20 adults, age 45-80 years, with msTBI. Ten were in aerobic exercise training (AET) program and 10 in a stretching and toning (SAT) program. The AET group was instructed to exercise based on their estimated maximal heart rate (HR) for 150 minutes weekly. The SAT group was to stretch for the same target time without significantly increasing HR or level of exertion. Outcome measures were Traumatic Brain Injury Quality of Life (TBI-QOL) for global, cognitive, emotional, and social health, Patient Health Questionnaire-9 (PHQ-9) for depressive symptoms, and Pittsburgh Sleep Quality Index (PSQI) for sleep quality. RESULTS: AET was associated with improved self-reported cognitive health and sleep compared to SAT. Moderate to large, positive effect sizes were also observed in the AET group in the QOL categories of global, emotional, and social health, and depressive symptoms. CONCLUSIONS: This study offers preliminary evidence that AET may improve health-related QOL, especially for cognition and sleep, in middle-aged and older adults with msTBI.

Respiratory Event-Induced Blood Pressure Oscillations Vary by Sleep Stage in Sleep Apnea Patients.

Obstructive sleep apnea (OSA) pathologically stresses the cardiovascular system. Apneic events cause significant oscillatory surges in nocturnal blood pressure (BP). Trajectories of these surges vary widely. This variability challenges the quantification, characterization, and mathematical modeling of BP surge dynamics. We present a method of aggregating trajectories of apnea-induced BP surges using a sample-by-sample averaging of continuously recorded BP. We applied the method to recordings of overnight BP (average total sleep time: 4.77 ± 1.64 h) for 10 OSA patients (mean AHI: 63.5 events/h; range: 18.3-105.4). We studied surges in blood pressure due to obstructive respiratory events separated from other such events by at least 30 s (274 total events). These events increased systolic (SBP) and diastolic (DBP) BP by 19 ± 7.1 mmHg (14.8%) and 11 ± 5.6 mmHg (15.5%), respectively, relative to mean values during wakefulness. Further, aggregated SBP and DBP peaks occurred on average 9 s and 9.5 s after apnea events, respectively. Interestingly, the amplitude of the SBP and DBP peaks varied across sleep stages, with mean peak ranging from 128.8 ± 12.4 to 166.1 ± 15.5 mmHg for SBP and from 63.1 ± 8.2 to 84.2 ± 9.4 mmHg for DBP. The aggregation method provides a high level of granularity in quantifying BP oscillations from OSA events and may be useful in modeling autonomic nervous system responses to OSA-induced stresses.

In Situ Investigation of Upper Airway Occlusion in Sleep Disordered Breathing Using Ultrasonic Transducer Arrays.

This work presents a novel application of ultrasound for the real-time, non-invasive investigation of occlusion of the upper airway during events of obstructive sleep apnea/hypopnea syndrome. It is hypothesized that ultrasonic pulses applied to the neck during apneic events produce spectral and temporal features that can detect apnea occurrence. Theoretical models of ultrasound propagation and an in vitro test were conducted to test this hypothesis in both transmission and reflection modes. Complete specifications and technical details of the system design and fabrication, which is mounted on each subject's neck, are presented, including the methodology. Nine patients (seven male and two female, mean age of 42 years, with a range of 25 to 56 years, and body mass index 37.6 ± 6.6 kg/m2) were recruited for a full night study, which included simultaneous nocturnal polysomnography for the validation of the results. Nine temporal features and four spectral features were extracted from the envelope of the received pulse waveform. These were used to compute 26 metrics to quantify the changes in the ultrasonic waveforms between normal breathing and apneic events. The statistical analysis of the collected ultrasonic data showed that at least two or more of the proposed features could detect apneic events in all subjects. The findings establish the feasibility of the proposed method as a cost-effective and non-invasive OSAHS screening tool.

Usability of a two-way personalized mobile trainer system in a community-based exercise program for adults with chronic traumatic brain injury.

OBJECTIVE: To examine the usability of an Apple Watch-based, two-way Personalized Mobile Trainer (PMT) in community-based exercise programs for individuals with chronic traumatic brain injury (cTBI). METHODS: This is a prospective pilot study. Twenty participants with cTBI aged 46-73 were enrolled in a 3-month individualized exercise program. After one in-person training session on PMT and exercise program, participants were prescribed either aerobic exercise training (AET) or stretching and toning (SAT) performed at home. The PMT was used to remotely deliver updated exercise prescription, track exercise progress, and communicate with the participants. The primary outcome was compliance with the exercise programs. RESULTS: All the participants completed the assigned exercise program with an average compliance of 76%. Nineteen (95%) participants were able to use the PMT properly during exercise sessions. After 3 months of training, the AET trended toward maintaining exercise endurance when compared with the SAT group (0.3% vs -4%, p = 0.14) with a medium effect size of 0.43. CONCLUSION: Using the PMT system to support and track exercise in community-based exercise programs is feasible. The PMT may promote compliance with the training program but testing its effectiveness with larger trials is warranted.

A Comparison of Alternative Approaches to MR Cardiac Triggering: A Pilot Study at 3 Tesla.

This pilot comparative study evaluates the usability of the alternative approaches to magnetic resonance (MR) cardiac triggering based on ballistocardiography (BCG): fiber-optic sensor (O-BCG) and pneumatic sensor (P-BCG). The comparison includes both the objective and subjective assessment of the proposed sensors in comparison with a gold standard of ECG-based triggering. The objective evaluation included several image quality assessment (IQA) parameters, whereas the subjective analysis was performed by 10 experts rating the diagnostic quality (scale 1 - 3, 1 corresponding to the best image quality and 3 the worst one). Moreover, for each examination, we provided the examination time and comfort rating (scale 1 - 3). The study was performed on 10 healthy subjects. All data were acquired on a 3 T SIEMENS MAGNETOM Prisma. In image quality analysis, all approaches reached comparable results, with ECG slightly outperforming the BCG-based methods, especially according to the objective metrics. The subjective evaluation proved the best quality of ECG (average score of 1.68) and higher performance of P-BCG (1.97) than O-BCG (2.03). In terms of the comfort rating and total examination time, the ECG method achieved the worst results, i.e. the highest score and the longest examination time: 2.6 and 10:49 s, respectively. The BCG-based alternatives achieved comparable results (P-BCG 1.5 and 8:06 s; OBCG 1.9, 9:08 s). This study confirmed that the proposed BCG-based alternative approaches to MR cardiac triggering offer comparable quality of resulting images with the benefits of reduced examination time and increased patient comfort.

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part II: Brain Signals.

As it was mentioned in the previous part of this work (Part I)-the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work-various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.

Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach-Part III: Other Biosignals.

Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part I: Cardiac Signals.

Advanced signal processing methods are one of the fastest developing scientific and technical areas of biomedical engineering with increasing usage in current clinical practice. This paper presents an extensive literature review of the methods for the digital signal processing of cardiac bioelectrical signals that are commonly applied in today's clinical practice. This work covers the definition of bioelectrical signals. It also covers to the extreme extent of classical and advanced approaches to the alleviation of noise contamination such as digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation and wavelet transform.

Quantification of Nocturnal Blood Pressure Oscillations Induced by Sleep Disordered Breathing.

We present an approach to quantifying nocturnal blood pressure (BP) variations that are elicited by sleep disordered breathing (SDB). A sample-by-sample aggregation of the dynamic BP variations during normal breathing and BP oscillations prompted by apnea episodes is performed. This approach facilitates visualization and analysis of BP oscillations. Preliminary results from analysis of a full night study of 7 SDB subjects (5 Male 2 Female, 52±5.6 yrs., Body Mass Index 36.4±7.4 kg/m2, Apnea-Hypopnea Index 69.1±26.8) are presented. Aggregate trajectory and quantitative values for changes in systolic blood pressure (SBP) and diastolic blood pressure (DBP) concomitant with obstructive apnea episodes are presented. The results show 19.4 mmHg (15.3%) surge in SBP and 9.4 mmHg (13.6%) surge in DBP compared to their respective values during normal breathing (p<0.05). Further, the peak of the surge in SBP and DBP occurred about 9s and 7s, respectively, post the end of apnea events. The return of SBP and DBP to baseline values displays a decaying oscillatory pattern.

A Review of Signal Processing Techniques for Non-Invasive Fetal Electrocardiography.

Fetal electrocardiography (fECG) is a promising alternative to cardiotocography continuous fetal monitoring. Robust extraction of the fetal signal from the abdominal mixture of maternal and fetal electrocardiograms presents the greatest challenge to effective fECG monitoring. This is mainly due to the low amplitude of the fetal versus maternal electrocardiogram and to the non-stationarity of the recorded signals. In this review, we highlight key developments in advanced signal processing algorithms for non-invasive fECG extraction and the available open access resources (databases and source code). In particular, we highlight the advantages and limitations of these algorithms as well as key parameters that must be set to ensure their optimal performance. Improving or combining the current or developing new advanced signal processing methods may enable morphological analysis of the fetal electrocardiogram, which today is only possible using the invasive scalp electrocardiography method.

Estimation of cerebral blood flow velocity during breath-hold challenge using artificial neural networks.

The effect of untreated Obstructive Sleep Apnoea (OSA) on cerebral haemodynamics and CA impairment is an active field of research interest. A breath-hold challenge is usually used in clinical and research settings to simulate cardiovascular and cerebrovascular changes that mimic OSA events. This work utilises temporal arterial oxygen saturation (SpO2) and photoplethysmography (PPG) signals to estimate the temporal cerebral blood flow velocity (CBFv) waveform. Measurements of CBFv, SpO2, and PPG, were acquired concurrently from volunteers performing two different protocols of breath-hold challenge in the supine position. Past values of the SpO2 and PPG signals were used to estimate the current values of CBFv using different permutations and topologies of supervised learning with shallow artificial neural networks (ANNs). The measurements from one protocol were used to train the ANNs and find the optimum topologies, which in turn were tested using the other protocol. Data collected from 10 normotensive, healthy subjects (four females, age 28.5 ±â€¯6.1 years, Body Mass Index (BMI) 24.0 ±â€¯4.7 kg/m2) were used in this study. The results show that different subjects have different optimum topologies for ANNs, thus indicating the effects of inter-subject variability on ANNs. Successfully reconstructed blind waveforms for the same subject group in the second protocol showed a reasonable accuracy of 60-80% estimation compared to the measured waveforms. HYPOTHESIS: Temporal waveforms for SpO2 and PPG contain adequate information to estimate the temporal CBFv waveform using ANNs. METHODOLOGY: Concurrent measurements of SpO2 and PPG using pulse oximetry from the forehead and CBFv from the middle cerebral artery (MCA) using transcranial Doppler (TCD) were recorded from healthy, normotensive subjects performing a breath-hold challenge. The breath-hold challenge mimicked the cerebrovascular response to apnoea, and was recorded by measuring CBFv in MCA. Two protocols were used, each consisting of five breath-holding manoeuvres and differing in terms of the time between the five successive breath-holds. Using data from one protocol, several permutations of the temporal values of SpO2 and PPG signals were used as inputs to different ANN topologies, in order to train and find the optimum model. The optimum model was evaluated using the data from the other protocol as a blind dataset. RESULTS: Using the first protocol for training, optimum ANN configurations were found to be different for each subject, and accuracy of 75-87% was achieved. When these optimum ANN models were tested using the second protocol as a blind dataset, the accuracy achieved was around 60-80%. CONCLUSIONS: A novel approach employing temporal records of SpO2 and PPG can be used to estimate the CBFv waveform using ANNs with acceptable accuracy. Increases in the size and diversity of the population dataset and the use of features extracted from SpO2 and PPG signals are needed for generalisation of the method and potential future clinical applications.

An Investigation of Heartrate Sensing Accuracy by Wrist-Worn Fitness Tracking Devices.

Preliminary results of assessing the accuracy of heartrate measurement by four wrist-worn fitness tracking devices (WFT) during waking and stationary biking in five healthy subjects (2F; age 26.4±3.20 years; BMI 24.7±1.92 kg/m2) are reported. The results reveal that the accuracy varies depending on the WFT and exercise mode.

Dynamic Estimation of Cerebral Blood Flow Using Blood Pressure Signal in sleep Apnea Patients.

Monitoring apnea-induced cerebral blood flow (CBF) oscillations is of importance for assessing apnea patient brain health. Blood pressure (BP) oscillations during apnea can induce oscillations in CBF. Preliminary results of testing an Auto Regressive Moving Average model relating nocturnal CBP oscillations to nocturnal BP variations in 8 obstructive sleep apnea subjects (3 F, 55±8 yrs., BMI 34.2±7.85 kg/m2) showed that largest mean and standard deviation of the CBF estimation errors was 4.49±7.57 cm/s and maximum root mean squared of the errors was 8.80 cm/s. Hence, reasonable accuracy in estimating CBF from BP during sleep apnea events was observed.

Dynamic Estimation of Cerebral Blood Flow Using Photoplethysmography Signal during Simulated Apnea.

Monitoring apnea-induced cerebral blood flow oscillations is of importance for assessing apnea patient brain health. Using an autoregressive moving average model, peak and trough values of cerebral blood flow were estimated from a concurrently recorded forehead photoplethysmography signal. Preliminary testing of the method in 7 subjects (4 F, 32±4 yrs., BMI 24.57±3.87 kg/m2) using a breath hold paradigm for simulating apnea shows that maximum mean and standard deviation of the prediction error is -1.10±8.49 cm/s and the maximum root mean squared of the error is 8.92 cm/s.

Mathematical Modeling of Arterial Blood Pressure Using Photo- Plethysmography Signal in Breath-hold Maneuver.

recent research has shown that each apnea episode results in a significant rise of the beat-to-beat blood pressure followed by a drop to the pre-episode levels when patient resumes normal breathing. While the physiological implications of these repetitive and significant oscillations are still unknown, it is of interest to quantify them. Since current array of instruments deployed for polysomnography studies does not include beat-to-beat measurement of blood pressure, but includes oximetry which can supply pulsatile photoplethysmography (PPG) signal, in addition to percent oxygen saturation. Hence, we have investigated a new method for continuous estimation of systolic (SBP), diastolic (DBP), and mean (MBP) blood pressure waveforms from PPG. Peaks and troughs of PPG waveform are used as input to a 5th order autoregressive moving average model to construct estimates of SBP, DBP, and MBP waveforms. Since breath hold maneuvers are shown to faithfully simulate apnea episodes, we evaluated the performance of the proposed method in 7 subjects (4 F; $32 \pm 4$ yrs., BMI $24.57 \pm 3.87$ kg/m2) in supine position doing 5 breath holding maneuvers with 90s of normal breathing between them. The modeling error ranges were (all units are in mmHg $) 0.88 \pm 4.87$ to $- 2.19 \pm 5.73($ SBP); $0.29 \pm 2.39$ to $- 0.97 \pm 3.83($ DBP); and $- 0.42 \pm 2.64$ to $- 1.17 \pm 3.82($ MBP). The cross validation error ranges were $0.28 \pm 6.45$ to $- 1.74 \pm 6.55($ SBP); $0.09 \pm 3.37$ to $0.97 \pm 3.67($ DBP); and $0.33 \pm 4.34$ to $- 0.87 \pm 4.42($ MBP). The overall level of estimation error, as measured by the root mean squared of the model residuals, was less than 7 mmHg.

Arterial blood pressure feature estimation using photoplethysmography.

Continuous and noninvasive monitoring of blood pressure has numerous clinical and fitness applications. Current methods of continuous measurement of blood pressure are either invasive and/or require expensive equipment. Therefore, we investigated a new method for the continuous estimation of two main features of blood pressure waveform: systolic and diastolic pressures. The estimates were obtained from a photoplethysmography signal as input to the fifth order autoregressive moving average models. The performance of the method was evaluated using beat-to-beat full-wave blood pressure measurements from 15 young subjects, with no known cardiovascular disorder, in supine position as they breathed normally and also while they performed a breath-hold maneuver. The level of error in the modeling and prediction estimates during normal breathing and breath-hold maneuvers, as measured by the root mean square of the residuals, were less than 5 mmHg and 11 mm Hg, respectively. The mean of model residuals both during normal breathing and breath-hold maneuvers was considered to be less than 3.2 mmHg. The dependency of the accuracy of the estimates on the subject data was assessed by comparing the modeling errors for the 15 subjects. Less than 1% of the models showed significant differences (p < 0.05) from the other models, which indicates a high level of consistency among the models.

Phantom study evaluating detection of simulated upper airway occlusion using piezoelectric ultrasound transducers.

Obstructive sleep apnea/hypopnea syndrome (OSAHS) is characterized by repetitive narrowing or full collapse of the upper airway concomitant with continued respiratory effort during sleep lasting 10s or more. OSAHS is the most prevalent form of sleep-disordered breathing, affecting more than 17% of the middle-aged U.S. POPULATION: Hence, many individuals need to be tested for having OSAHS. Currently, detection of airway occlusion due to OSAHS is achieved by indirect measurements, often requiring multiple sensor types, such as a flow transducer combined with chest and abdomen plethysmography. The need for the use of multiple sensors in the current OSAHS detection systems adds to the cost and complexity of the current systems and associated procedures. Development of a simple sensor system that allows direct detection of airway occlusion is advantageous, as it simplifies detection of OSAHS and paves the way for home diagnosis of OSAHS. The utilization of ultrasonic transducers is attractive, as it is non-invasive and non-ionizing. We present a new ultrasound sensing system for direct detection of the occlusion in the upper airway in OSAHS patients during sleep. The system takes into consideration the constraints arising from the location of probing and the acoustic requirements for transducers. The physiological and theoretical backgrounds are presented for using ultrasonic pulses to detect the presence and degree of occlusion in the airway. The proposed methodology for creating an anthropomorphically-correct neck and airway phantoms to test the hypothesis and the results of the tests are presented. HYPOTHESIS: An ultrasonic signal transmitted through or reflected from an open airway will have different features compared to those associated with a partially or fully occluded airway. METHODOLOGY: A system, comprising a phantom model of the airway and neck with the approximate anatomical-correct dimensions and acoustic properties of the airway, is designed and built. It allows simulating fully open airway as well as hypopnea and apnea events. Further, it facilitates probing using multiple ultrasonic frequencies and transducer configurations for use with different neck sizes. Ultrasound waves are generated using a piezoelectric source to the model of the airway and received by piezoelectric receivers on the opposite side. Energy, the area under the curve, and the peak value of the received signal, are used to detect the airway occlusion. RESULTS: The amount of reflected ultrasonic energy from the phantom model of the airway back to the transmitting transducer reduces as the airway model occlusion increases. Also, transmitted signal through the airway model increases as the amount occlusion of the airway model increases. CONCLUSIONS: The results of this study support the hypothesis that it is feasible to use ultrasonic pulses to detect partial and full upper airway occlusion.

Dynamic modeling of apnea induced concurrent variations in arterial blood pressure and cerebral blood flow velocity.

Obstructive Sleep Apnea (OSA) is characterized by partial (hypopnea) or complete cessation (apnea) of airflow to the lungs during sleep. It has been previously reported that apnea episodes lead to significant rise in instantaneous blood pressure concomitant with a rise in cerebral blood flow velocity, indicating loss of cerebral autoregulation during the episodes. In this study, we have used Auto Regressive Moving Average model (ARMA (na, nb, nk)) to quantify OSA induced dynamic changes in cerebral blood flow velocity (CBFV) with beat to beat blood pressure (BP) as an input. BP and CBFV were recorded from 11 positively diagnosed sleep apnea subjects (6 Males, 5 Females; Age: 54.27±6.23 years, BMI:34.95±7.06kg/m2, AHI: 57.39±28.43). The results suggest that two separate models, ARMA (5, 9, 1) and ARMA (5, 10, 0) can be used to quantify dynamic CBFV variations during apneas with a duration of less than and greater than 30s respectively with reasonable accuracy (<;6% error).

A study of apnea induced covariations of cerebral blood flow and exhaled CO2.

Sleep apnea is identified by repetitive reduction or complete cessation of breathing during sleep. Sleep apnea affects cerebral hemodynamics and it is important to study this effect. Measuring cerebral blood flow during sleep is challenging due to the need to maintain a contact between the flow probe and the skull. It is hypothesized that there exists a relationship between the variations in the exhaled CO2 and Cerebral Blood Flow during sleep apnea. To test this hypothesis, the present study was conducted in two parts: simulated and nocturnal sleep study. 9 volunteer subjects (6 Male and 3 Female Age: 23.11±1.59 years BMI: 21.9±2.409kg/m2) participated in the simulated study and 10 volunteer subjects (9 Male and 1 Female Age: 50.2±7.48 years BMI: 31.541±4.56 kg/m2 AHI: 62.84±20.44) participated in a nocturnal sleep study. Analyzing full waveforms of cerebral blood flow velocity (CBFV) and exhaled CO2, the relationship between 4 metrics from CBFV and 2 metrics from exhaled CO2 were investigated. Although one metric pair showed statistically significant and relatively high correlation (ρ= 0.68 p=7.96×10-7) during the simulated study, the same was not observed during the nocturnal study. Therefore, the proposed hypothesis could not be proven.