The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century.

As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI's potential to mitigate these issues and aims to critically assess AI's integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI's transformative potential, this review equips researchers with a deeper understanding of AI's current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.

Blood Pressure Estimation Based on the Timing of the Arterial Reflected Waves by Oscillometric Pulse Wave Decomposition.

Recent innovations in oscillometric blood pressure (BP) estimation leveraging pulse transit time (PTT) show promise even when pulse amplitude is compromised. However, current PTT methods often require multiple sensors. This study introduces a novel approach for oscillometric BP estimation utilizing only a single sensor. Our method simplifies PTT measurement through decomposing oscillometric pulses into forward and reflected wave components. Notably, we found that the time interval between the forward and first reflected wave is shortest when cuff pressure is at the systolic level, while the interval between the first and second reflected waves is longest when the cuff pressure corresponds to the diastolic level. To validate our method, we extensively analyzed two datasets: the first comprised 150 arm oscillometric recordings from 10 healthy individuals, and the second included 425 wrist oscillometric recordings from 85 healthy individuals. Our analysis revealed minimal standard deviation errors for BP measurements: 7.03 mmHg (systolic) and 5.96 mmHg (diastolic) for arm recordings, and 7.98 mmHg (systolic) and 5.39 mmHg (diastolic) for wrist recordings. Across both datasets, the mean error for both systolic and diastolic BP measurements was merely 0.01 mmHg, underscoring the precision of the method. Comparative analysis against established methodologies further affirmed the superior performance of our approach, which also met the ANSI/AAMI/ISO81060-2 standard. While our dataset primarily comprises recordings from healthy individuals, further validation on diverse datasets encompassing challenging cases such as obesity, arterial stiffness, and atrial fibrillation is imperative to evaluate its applicability across various clinical scenarios.

Hybrid Deep Morpho-Temporal Framework for Oscillometric Blood Pressure Measurement.

Oscillometric blood pressure (BP) measurement devices are widely utilized as the primary automated BP measurement tools in non-specialist environments. However, their accuracy and reliability vary under different settings and for different age groups and health conditions. An essential constraint of current oscillometric BP measurement devices is their analysis algorithms' incapacity to capture the BP information encoded in the pattern of recorded oscillometric pulses to its fullest extent. In this article, we propose a new 2D oscillometric data representation that enables a full characterization of arterial system and empowers the application of deep learning to extract the most informative features correlated with BP. A hybrid convolutional-recurrent neural network was developed to capture the oscillometric pulses morphological information as well as their temporal evolution over the cuff deflation period from the 2D structure, and estimate BP. The performance of the proposed method was verified on three oscillometric databases collected from the wrist and upper arms of 245 individuals. It was found that it achieves a mean error and a standard deviation of error of as low as 0.08 mmHg and 2.4 mmHg in the estimation of systolic BP, and 0.04 mmHg and 2.2 mmHg in the estimation of diastolic BP, respectively. Our proposed method outperformed the state-of-the-art techniques and satisfied the current international standards for BP monitors by a wide margin. The proposed method shows promise toward robust and objective BP estimation in a variety of patients and monitoring situations.

Unsupervised ECG Analysis: A Review.

Electrocardiography is the gold standard technique for detecting abnormal heart conditions. Automatic detection of electrocardiogram (ECG) abnormalities helps clinicians analyze the large amount of data produced daily by cardiac monitors. As thenumber of abnormal ECG samples with cardiologist-supplied labels required to train supervised machine learning models is limited, there is a growing need for unsupervised learning methods for ECG analysis. Unsupervised learning aims to partition ECG samples into distinct abnormality classes without cardiologist-supplied labels-a process referred to as ECG clustering. In addition to abnormality detection, ECG clustering has recently discovered inter and intra-individual patterns that reveal valuable information about the whole body and mind, such as emotions, mental disorders, and metabolic levels. ECG clustering can also resolve specific challenges facing supervised learning systems, such as the imbalanced data problem, and can enhance biometric systems. While several reviews exist on supervised ECG systems, a comprehensive review of unsupervised ECG analysis techniques is still lacking. This study reviews ECG clustering techniques developed mainly in the last decade. The focus will be on recent machine learning and deep learning algorithms and their practical applications. We critically review and compare these techniques, discuss their applications and limitations, and provide future research directions. This review provides further insights into ECG clustering and presents the necessary information required to adopt the appropriate algorithm for a specific application.

Deep Learning Forecasts the Occurrence of Sleep Apnea from Single-Lead ECG.

OBJECTIVES: Sleep apnea is the most common sleep disorder that leads to serious health complications if not treated early. Forecasting apnea occurrence ahead in time provides the opportunity to take appropriate actions to control and manage it. METHODS: A novel framework for forecasting the occurrence of apnea from single-lead electrocardiogram (ECG) based on deep recurrent neural networks is proposed. ECG R-peak amplitudes and R-R intervals are extracted and aligned using power spectral analysis, and recurrent deep learning models are developed to extract the most predictive ECG features and forecast the occurrence of apnea. RESULTS: The performance of the proposed approach was validated in forecasting apnea events up to five minutes in future on a dataset of 70 sleep recordings. A forecasting accuracy of up to 94.95% was achieved which was higher than the performance of conventional multilayer perceptron (p < 0.05) and other state-of-the-art techniques. CONCLUSIONS: The proposed deep learning approach was successful in forecasting the occurrence of sleep apnea from single-lead ECG. It can therefore be adopted in wearable sleep monitors for the management of sleep apnea. Our developed algorithms are publicly available on GitHub.

Survival Analysis in Cognitively Normal Subjects and in Patients with Mild Cognitive Impairment Using a Proportional Hazards Model with Extreme Gradient Boosting Regression.

BACKGROUND: Evaluating the risk of Alzheimer's disease (AD) in cognitively normal (CN) and patients with mild cognitive impairment (MCI) is extremely important. While MCI-to-AD progression risk has been studied extensively, few studies estimate CN-to-MCI conversion risk. The Cox proportional hazards (PH), a widely used survival analysis model, assumes a linear predictor-risk relationship. Generalizing the PH model to more complex predictor-risk relationships may increase risk estimation accuracy. OBJECTIVE: The aim of this study was to develop a PH model using an Xgboost regressor, based on demographic, genetic, neuropsychiatric, and neuroimaging predictors to estimate risk of AD in patients with MCI, and the risk of MCI in CN subjects. METHODS: We replaced the Cox PH linear model with an Xgboost regressor to capture complex interactions between predictors, and non-linear predictor-risk associations. We endeavored to limit model inputs to noninvasive and more widely available predictors in order to facilitate future applicability in a wider setting. RESULTS: In MCI-to-AD (n = 882), the Xgboost model achieved a concordance index (C-index) of 84.5%. When the model was used for MCI risk prediction in CN (n = 100) individuals, the C-index was 73.3%. In both applications, the C-index was statistically significantly higher in the Xgboost in comparison to the Cox PH model. CONCLUSION: Using non-linear regressors such as Xgboost improves AD dementia risk assessment in CN and MCI. It is possible to achieve reasonable risk stratification using predictors that are relatively low-cost in terms of time, invasiveness, and availability. Future strategies for improving AD dementia risk estimation are discussed.

ACE2 as a Potential Target for Management of Novel Coronavirus (nCoV- 2019).

A novel coronavirus termed nCoV-2019 that caused an epidemic of acute respiratory syndrome in humans was first detected in Wuhan, China, in December 2019. nCoV-2019 resulted in thousands of cases of lethal disease all around the world. Unfortunately, there is no specific treatment yet, so a better understanding of the pathobiology of the disease can be helpful. The renin-angiotensin system and its products have several important physiological actions. On the other hand, this system is involved in the pathogenesis of various diseases. In this context, this review article will briefly discuss insights for understanding the role of the angiotensin-converting enzyme 2 (ACE2) receptor as a potentially attractive target for the nCoV-2019-induced acute respiratory syndrome.

Intelligent Oscillometric System for Automatic Detection of Peripheral Arterial Disease.

Peripheral arterial disease (PAD) is a progressing arterial disorder that is associated with significant morbidity and mortality. The conventional PAD detection methods are invasive, cumbersome, or require expensive equipment and highly trained technicians. Here, we propose a new automated, noninvasive, and easy-to-use method for the detection of PAD based on characterizing the arterial system by applying an external varying pressure using a cuff. The superposition of the internal arterial pressure and the externally applied pressure were measured and mathematically modeled as a function of cuff pressure. A feature-based learning algorithm was then designed to identify PAD patterns by analyzing the parameters of the derived mathematical models. Genetic algorithm and principal component analysis were employed to select the best predictive features distinguishing PAD patterns from normal. A RUSBoost ensemble model using neural network as the base learner was designed to diagnose PAD from genetic algorithm selected features. The proposed method was validated on data collected from 14 PAD patients and 19 healthy individuals. It achieved a high accuracy, sensitivity, and specificity of 91.4%, 90.0%, and 92.1%, respectively, in detecting PAD. The effect of age, a confounding factor that may have impacted our analyzes, was not considered in this study. The proposed method shows promise toward noninvasive and accurate detection of PAD and can be integrated into routine oscillometric blood pressure measurements.

Stress, sleep, and autonomic function in healthy adolescent girls and boys: Findings from the NCANDA study.

OBJECTIVES: Starting in adolescence, female sex is a strong risk factor for the development of insomnia. Reasons for this are unclear but could involve altered stress reactivity and/or autonomic nervous system (ANS) dysregulation, which are strongly associated with the pathophysiology of insomnia. We investigated sex differences in the effect of stress on sleep and ANS activity in adolescents, using the first night in the laboratory as an experimental sleep-related stressor. DESIGN: Repeated measures (first night vs. a subsequent night) with age (older/younger) and sex (males/females) as between factors. SETTING: Recordings were performed at the human sleep laboratory at SRI International. PARTICIPANTS: One hundred six healthy adolescents (Age, mean ± SD: 15.2 ± 2.0 years; 57 boys). MEASURES: Polysomnographic sleep, nocturnal heart rate (HR), and frequency-domain spectral ANS HR variability (HRV) indices. RESULTS: Boys and girls showed a first-night effect, characterized by lower sleep efficiency, lower %N1 and %N2 sleep, more wake after sleep onset and %N3 sleep, altered sleep microstructure (increased high-frequency sigma and Beta1 electroencephalographic activity), and reduced vagal activity (P < .05) on the first laboratory night compared to a subsequent night. The first night ANS stress effect (increases in HR and suppression in vagal HRV during rapid eye movement sleep) was greater in girls than boys (P < .05). CONCLUSIONS: Sleep and ANS activity were altered during the first laboratory night in adolescents, with girls exhibiting greater ANS alterations than boys. Findings suggest that girls may be more vulnerable than boys to sleep-specific stressors, which could contribute to their increased risk for developing stress-related sleep disturbances.

Impact of evening alcohol consumption on nocturnal autonomic and cardiovascular function in adult men and women: a dose-response laboratory investigation.

STUDY OBJECTIVES: To investigate the dose-dependent impact of moderate alcohol intake on sleep-related cardiovascular (CV) function, in adult men and women. METHODS: A total of 26 healthy adults (30-60 years; 11 women) underwent 3 nights of laboratory polysomnographic (PSG) recordings in which different doses of alcohol (low: 1 standard drink for women and 2 drinks for men; high: 3 standard drinks for women and 4 drinks for men; placebo: no alcohol) were administered in counterbalanced order before bedtime. These led to bedtime average breath alcohol levels of up to 0.02% for the low doses and around 0.05% for the high doses. Autonomic and CV function were evaluated using electrocardiography, impedance cardiography, and beat-to-beat blood pressure monitoring. RESULTS: Presleep alcohol ingestion resulted in an overall increase in nocturnal heart rate (HR), suppressed total and high-frequency (vagal) HR variability, reduced baroreflex sensitivity, and increased sympathetic activity, with effects pronounced after high-dose alcohol ingestion (p's < 0.05); these changes followed different dose- and measure-dependent nocturnal patterns in men and women. Systolic blood pressure showed greater increases during the morning hours of the high-alcohol dose night compared to the low-alcohol dose night and placebo, in women only (p's < 0.05). CONCLUSIONS: Acute evening alcohol consumption, even at moderate doses, has marked dose- and time-dependent effects on sleep CV regulation in adult men and women. Further studies are needed to evaluate the potential CV risk of repeated alcohol-related alterations in nighttime CV restoration in healthy individuals and in those at high risk for CV diseases, considering sex and alcohol dose and time effects.

The falling asleep process in adolescents.

STUDY OBJECTIVES: To investigate the pre-sleep psychophysiological state and the arousal deactivation process across the sleep onset (SO) transition in adolescents. METHODS: Data were collected from a laboratory overnight recording in 102 healthy adolescents (48 girls, 12-20 years old). Measures included pre-sleep self-reported cognitive/somatic arousal, and cortical electroencephalographic (EEG) and electrocardiographic activity across the SO transition. RESULTS: Adolescent girls, compared with boys, reported higher pre-sleep cognitive activation (p = 0.025) and took longer to fall asleep (p < 0.05), as defined with polysomnography. Girls also showed a less smooth progression from wake-to-sleep compared with boys (p = 0.022). In both sexes, heart rate (HR) dropped at a rate of ~0.52 beats per minute in the 5 minutes preceding SO, and continued to drop, at a slower rate, during the 5 minutes following SO (p < 0.05). Older girls had a higher HR overall in the pre-sleep period and across SO, compared to younger girls and boys (p < 0.05). The EEG showed a progressive cortical synchronization, with increases in Delta relative power and reductions in Alpha, Sigma, Beta1, and Beta2 relative powers (p < 0.05) in the approach to sleep, in both sexes. Delta relative power was lower and Theta, Alpha, and Sigma relative powers were higher in older compared to younger adolescents at bedtime and across SO (p < 0.05). CONCLUSIONS: Our findings show the dynamics of the cortical-cardiac de-arousing process across the SO transition in a non-clinical sample of healthy adolescents. Findings suggest a female-specific vulnerability to inefficient sleep initiation, which may contribute to their greater risk for developing insomnia.

Automatic analysis of pre-ejection period during sleep using impedance cardiogram.

The pre-ejection period (PEP) is a valid index of myocardial contractility and beta-adrenergic sympathetic control of the heart defined as the time between electrical systole (ECG Q wave) to the initial opening of the aortic valve, estimated as the B point on the impedance cardiogram (ICG). B-point detection accuracy can be severely impacted if ICG cardiac cycles corrupted by motion artifact, noise, or electrode displacement are included in the analyses. Here, we developed new algorithms to detect and exclude corrupted ICG cycles by analyzing their level of activity. PEP was then estimated and analyzed on ensemble-averaged clean ICG cycles using an automatic algorithm previously developed by the authors for the detection of B point in awake individuals. We investigated the algorithms' performance relative to expert visual scoring on long-duration data collected from 20 participants during overnight recordings, where the quality of ICG could be highly affected by movement artifacts and electrode displacements and the signal could also vary according to sleep stage and time of night. The artifact rejection algorithm achieved a high accuracy of 87% in detection of expert-identified corrupted ICG cycles, including those with normal amplitude as well as out-of-range values, and was robust to different types and levels of artifact. Intraclass correlations for concurrent validity of the B-point detection algorithm in different sleep stages and in-bed wakefulness exceeded 0.98, indicating excellent agreement with the expert. The algorithms show promise toward sleep applications requiring accurate and reliable automatic measurement of cardiac hemodynamic parameters.

Electroencephalographic Slow-Wave Activity During Sleep in Different Phases of Blood Pressure and Respiration Oscillations.

Sleep is characterized by dynamic coupling between central (CNS) and peripheral autonomic (ANS) nervous systems. However, further research is needed to better understand the multiple interactions occurring among electroencephalographic (EEG) features and respiratory and cardiovascular (CV) outputs modulated by the ANS during sleep. Here, we developed new methods to study EEG slow-wave activity (SWA) during non-rapid eye movement (NREM) sleep with respect to the phases of peripheral oscillations. EEG, respiration, and continuous blood pressure signals recorded from 20 participants were analyzed. Digital filters, designed to decompose the signals into different frequency bands, and the Hilbert transform were applied to estimate the instantaneous phases and frequencies of the peripheral oscillations. The peripheral oscillations were categorized into four phases representing up and down states. EEG delta power (synchronized SWA) was computed and compared across these phases during NREM sleep. Results show that EEG delta power is higher during down phases of slow and respiratory frequency components of blood pressure and during up phases of respiration, suggestive of CNS-ANS coupling during NREM sleep. The developed techniques provide the preliminary framework to further analyze and interpret complex interactions between cortical and cardiac oscillations and their synchrony.

Automatic Artifact Detection in Impedance Cardiogram Using Pulse Similarity Index.

Impedance cardiography (ICG) is a noninvasive technique for evaluation of cardiac hemodynamic parameters such as cardiac output and pre-ejection period. However, the sensitivity of the technique to motion artifact, electrode displacement, and cardiovascular pathologies can severely impact the accuracy of hemodynamic parameter estimates. In this paper, we proposed a new algorithm for the automatic detection and exclusion of corrupted ICG cardiac cycles by defining a pulse similarity index that quantifies the level of pulse corruption and its diversion from a typical-shaped pulse. The index considers different features (activity, structure, shape, and pattern) of the ICG cardiac cycles. The algorithm is compared on sleep data collected from 20 participants against expert identified corrupted cycles. The artifact rejection algorithm achieved a high accuracy of 96% in detection of expert-identified corrupted ICG cycles, including those with normal amplitude as well as out-of-range values, and was robust to different types and levels of artifact. The algorithm shows promise toward applications requiring accurate and reliable automatic measurement of cardiac hemodynamic parameters from prolonged data sets.

Changes in heart rate and blood pressure during nocturnal hot flashes associated with and without awakenings.

Hot flashes (HFs) are a hallmark of menopause in midlife women. They are beyond bothersome symptoms, having a profound impact on quality of life and wellbeing, and are a potential marker of cardiovascular (CV) disease risk. Here, we investigated the impact on CV functioning of single nocturnal HFs, considering whether or not they were accompanied by arousals or awakenings. We investigated changes in heart rate (HR, 542 HFs), blood pressure (BP, 261 HFs), and pre-ejection period (PEP, 168 HFs) across individual nocturnal physiological HF events in women in the menopausal transition or post-menopause (age: 50.7 ± 3.6 years) (n = 86 for HR, 45 for BP, 27 for PEP). HFs associated with arousals/awakenings (51.1%), were accompanied by an increase in systolic (SBP; ~6 mmHg) and diastolic (DBP; ~5 mmHg) BP and HR (~20% increase), sustained for several minutes. In contrast, HFs occurring in undisturbed sleep (28.6%) were accompanied by a drop in SBP and a marginal increase in HR, likely components of the heat dissipation response. All HFs were accompanied by decreased PEP, suggesting increased cardiac sympathetic activity, with a prolonged increase for HFs associated with sleep disruption. Older age predicted greater likelihood of HF-related sleep disturbance. HFs were less likely to wake a woman in rapid-eye-movement and slow-wave sleep. Findings show that HFs associated with sleep disruption, which are in the majority and more likely in older women, lead to increases in HR and BP, which could have long-term impact on nocturnal CV restoration in women with multiple HFs.

Automatic Detection of Hot Flash Occurrence and Timing from Skin Conductance Activity.

Hot flashes (HF) are intense, transient feelings of heat usually accompanied with flushed skin and sweating that are experienced by women around the time of menopause. HFs are associated with poor quality of life and increased cardiovascular risk. Automatic detection of HF occurrence and precise timing of HF onset could provide unique insight into the physiology of the HF and its effect on the cardiovascular system. A novel automatic algorithm is proposed for the detection of HFs occurrence and timing from the sternal skin conductance signal that is robust to noise and artifacts. The method is based on the gold standard rule (2µS rise in skin conductance within 30 s) and considers several conditions based on the skin conductance level and its derivative to reject unwanted events. ECG-derived heart rate pattern variations are studied prior to the detected HF onset. The algorithm is validated against expert detected HFs over 200 hours of sleep data collected from 12 perimenopausal women. It achieved a total accuracy of 93% and a total error of 3% in HF detection. It was observed that heart rate increased before the onset of 80% of the HFs occurring in undisturbed sleep. Application of this algorithm along with fusion of other simultaneously recorded physiological measures has the potential to advance understanding of the HF.

Toward a better noninvasive assessment of preejection period: A novel automatic algorithm for B-point detection and correction on thoracic impedance cardiogram.

Impedance cardiography is the most common clinically validated, noninvasive method for determining the timing of the opening of the aortic valve, an important event used for measuring preejection period, which reflects sympathetic beta-adrenergic influences on the heart. Automatic detection of the exact time of the opening of the aortic valve (B point on the impedance cardiogram) has proven to be challenging as its appearance varies between and within individuals and may manifest as a reversal, inflection, or rapid slope change of the thoracic impedance derivative's (dZ/dt) rapid rise. Here, a novel automatic algorithm is proposed for the detection of the B point by finding the main rapid rise of the dZ/dt signal, which is due to blood ejection. Several conditions based on zero crossings, minima, and maxima of the dZ/dt signal and its derivatives are considered to reject any unwanted noise and artifacts and select the true B-point location. The detected B-point locations are then corrected by modeling the B-point time data using forward and reverse autoregressive models. The proposed algorithm is validated against expert-detected B points and is compared with different conventional methods; it significantly outperforms them by at least 54% in mean error, 30% in mean absolute error, and 27% in standard deviation of error. This algorithm can be adopted in ambulatory studies requiring beat-to-beat evaluation of cardiac hemodynamic parameters over extended time periods where expert scoring is not feasible.

Characteristic Ratio-Independent Arterial Stiffness-Based Blood Pressure Estimation.

Noninvasive blood pressure (BP) measurement is an important tool for managing hypertension and cardiovascular disease. However, automated noninvasive BP measurement devices, which are usually based on the oscillometric method, do not always provide accurate estimation of BP. It has been found that change in arterial stiffness (AS) is an underlying mechanism of disagreement between an oscillometric BP monitor and a sphygmomanometer. This problem is addressed by incorporating parameters related to AS in the algorithm for BP measurement. Pulse transit time (PTT) is first used to estimate AS parameters, which are fixed into a model of the oscillometric envelope. This model can then be used to perform curve fitting to the measured signal using only four parameters: systolic BP, diastolic BP, mean BP, and lumen area at zero transmural pressure. The proposed technique is independent of the experimentally determined characteristic ratios that are commonly used in existing oscillometric methods. The accuracy of the proposed technique was evaluated by comparing with the same model without incorporation of AS, and with reference BP device measurements. The new method achieved standard deviation of error less than 8 mmHg and mean error less than 5 mmHg. The results show consistency with ANSI/AAMI SP-10 standard for noninvasive BP measurement techniques.

Event Recognition for Contactless Activity Monitoring Using Phase-Modulated Continuous Wave Radar.

OBJECTIVES: The use of remote sensing technologies such as radar is gaining popularity as a technique for contactless detection of physiological signals and analysis of human motion. This paper presents a methodology for classifying different events in a collection of phase modulated continuous wave radar returns. The primary application of interest is to monitor inmates where the presence of human vital signs amidst different, interferences needs to be identified. METHODS: A comprehensive set of features is derived through time and frequency domain analyses of the radar returns. The Bhattacharyya distance is used to preselect the features with highest class separability as the possible candidate features for use in the classification process. The uncorrelated linear discriminant analysis is performed to decorrelate, denoise, and reduce the dimension of the candidate feature set. Linear and quadratic Bayesian classifiers are designed to distinguish breathing, different human motions, and nonhuman motions. The performance of these classifiers is evaluated on a pilot dataset of radar returns that contained different events including breathing, stopped breathing, simple human motions, and movement of fan and water. RESULTS: Our proposed pattern classification system achieved accuracies of up to 93% in stationary subject detection, 90% in stop-breathing detection, and 86% in interference detection. CONCLUSION: Our proposed radar pattern recognition system was able to accurately distinguish the predefined events amidst interferences. SIGNIFICANCE: Besides inmate monitoring and suicide attempt detection, this paper can be extended to other radar applications such as home-based monitoring of elderly people, apnea detection, and home occupancy detection.

Bayesian fusion algorithm for improved oscillometric blood pressure estimation.

A variety of oscillometric algorithms have been recently proposed in the literature for estimation of blood pressure (BP). However, these algorithms possess specific strengths and weaknesses that should be taken into account before selecting the most appropriate one. In this paper, we propose a fusion method to exploit the advantages of the oscillometric algorithms and circumvent their limitations. The proposed fusion method is based on the computation of the weighted arithmetic mean of the oscillometric algorithms estimates, and the weights are obtained using a Bayesian approach by minimizing the mean square error. The proposed approach is used to fuse four different oscillometric blood pressure estimation algorithms. The performance of the proposed method is evaluated on a pilot dataset of 150 oscillometric recordings from 10 subjects. It is found that the mean error and standard deviation of error are reduced relative to the individual estimation algorithms by up to 7 mmHg and 3 mmHg in estimation of systolic pressure, respectively, and by up to 2 mmHg and 3 mmHg in estimation of diastolic pressure, respectively.