P.-F. Migeotte is with the Department of Cardiology, Universite Libre de Bruxelles 1050, Brussels, Belgium (e-mail: Pierre-Francois.Migeotte@ulb.ac.be).K.-S. Park is with the Department of Biomedical Engineering, Seoul National University, Seoul 110-799, Korea (e-mail: kspark@bmsil.snu.ac.kr).M. Etemadi is with the Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco, San Francisco, CA 94143 USA (e-mail: mozziyar.etemadi@ucsf.edu).K. Tavakolian is with the Department of Electrical Engineering, University of North Dakota, Grand Forks, ND 58202 USA (e-mail: kouhyart@gmail.com).R. Casanella is with the Instrumentation, Sensors, and Interfaces Group, Universitat Politecnica de Catalunya, 08034 Barcelona, Spain (e-mail: ramon. casanella@upc.edu).J. Zanetti is with Acceleron Medical Systems, Arkansaw, WI 54721 USA (e-mail: jmzsenior@gmail.com).J. Tank is with the Klinsche Pharmakologie, Medizinische Hochschule Hannover, 30625 Hannover, Germany (e-mail: Tank.Jens@mh-hannover.de).I. Funtova is with the

This paper presents an overview of seismocardiography (SCG) as a noninvasive cardiology method. The paper represents a brief historical background to the SCG, an assessment of the technology at present, and an evaluation of the challenges we must address. These challenges include the development and clarification of definitions, standards, and annotations.

Parkinson’s disease (PD) patients regularly exhibit abnormal gait patterns. Automated differentiation of abnormal gait from normal gait can serve as a potential tool for early diagnosis as well as monitoring the effect of PD treatment. The aim of current study is to differentiate PD patients from healthy controls, on the basis of features derived from plantar vertical ground reaction force (VGRF) data during walking at normal pace. The current work presents a comprehensive study highlighting the efficacy of different machine learning classifiers towards devising an accurate prediction system. Selection of meaningful feature based on sequential forward feature selection, the swing time, stride time variability, and center of pressure features facilitated successful classification of control and PD gaits. Support Vector Machine (SVM), K-nearest neighbor (KNN), random forest, and decision trees classifiers were used to build the prediction model. We found that SVM with cubic kernel outperformed other classifiers with an accuracy of 93.6%, the sensitivity of 93.1%, and specificity of 94.1%. In comparison to other studies, utilizing same dataset, our designed prediction system improved the classification performance by approximately 10%. The results of the current study underscore the ability of the VGRF data obtained non-invasively from wearable devices, in combination with a SVM classifier trained on meticulously selected features, as a tool for diagnosis of PD and monitoring effectiveness of therapy post pathology.

The presented algorithm could be used for accurate and non-invasive estimation of cardiac time intervals.

The causal interaction between cardio-postural-musculoskeletal systems is critical in maintaining postural stability under orthostatic challenge. The absence or reduction of such interactions could lead to fainting and falls often experienced by elderly individuals. The causal relationship between systolic blood pressure (SBP), calf electromyography (EMG), and resultant center of pressure (COPr) can quantify the behavior of cardio-postural control loop. Convergent cross mapping (CCM) is a non-linear approach to establish causality, thus, expected to decipher nonlinear causal cardio-postural-musculoskeletal interactions. Data were acquired simultaneously from young participants (25 ± 2 years, n = 18) during a 10-minute sit-to-stand test. In the young population, skeletal muscle pump was found to drive blood pressure control (EMG → SBP) as well as control the postural sway (EMG → COPr) through the significantly higher causal drive in the direction towards SBP and COPr. Furthermore, the effect of aging on muscle pump activation associated with blood pressure regulation was explored. Simultaneous EMG and SBP were acquired from elderly group (69 ± 4 years, n = 14). A significant (p = 0.002) decline in EMG → SBP causality was observed in the elderly group, compared to the young group. The results highlight the potential of causality to detect alteration in blood pressure regulation with age, thus, a potential clinical utility towards detection of fall proneness.

Cardiac time intervals are important hemodynamic indices and provide information about left ventricular performance. Phonocardiography (PCG), impedance cardiography (ICG), and recently, seismocardiography (SCG) have been unobtrusive methods of choice for detection of cardiac time intervals and have potentials to be integrated into wearable devices. The main purpose of this study was to investigate the accuracy and precision of beat-to-beat extraction of cardiac timings from the PCG, ICG and SCG recordings in comparison to multimodal echocardiography (Doppler, TDI, and M-mode) as the gold clinical standard. Recordings were obtained from 86 healthy adults and in total 2,120 cardiac cycles were analyzed. For estimation of the pre-ejection period (PEP), 43% of ICG annotations fell in the corresponding echocardiography ranges while this was 86% for SCG. For estimation of the total systolic time (TST), these numbers were 43, 80, and 90% for ICG, PCG, and SCG, respectively. In summary, SCG and PCG signals provided an acceptable accuracy and precision in estimating cardiac timings, as compared to ICG.

Cardiovascular and postural control systems have been studied independently despite the increasing evidence showing the importance of cardiopostural interaction in blood pressure regulation. In this study, we aimed to assess the role of the cardiopostural interaction in relation to cardiac baroreflex in blood pressure regulation under orthostatic stress before and after mild exercise. Physiological variables representing cardiovascular control (heart rate and systolic blood pressure), lower limb muscle activation (electromyography), and postural sway (center of pressure derived from force and moment data during sway) were measured from 17 healthy participants (25 ± 2 yr, 9 men and 8 women) during a sit-to-stand test before and after submaximal exercise. The cardiopostural control (characterized by baroreflex-mediated muscle-pump effect in response to blood pressure changes, i.e., muscle-pump baroreflex) was assessed using wavelet transform coherence and causality analyses in relation to the baroreflex control of heart rate. Significant cardiopostural blood pressure control was evident counting for almost half of the interaction time with blood pressure changes that observed in the cardiac baroreflex (36.6-72.5% preexercise and 34.7-53.9% postexercise). Thus, cardiopostural input to blood pressure regulation should be considered when investigating orthostatic intolerance. A reduction of both cardiac and muscle-pump baroreflexes in blood pressure regulation was observed postexercise and was likely due to the absence of excessive venous pooling and a less stressed system after mild exercise. With further studies using more effective protocols evoking venous pooling and muscle-pump activity, the cardiopostural interaction could improve our understanding of the autonomic control system and ultimately lead to a more accurate diagnosis of cardiopostural dysfunctions. We examined the interaction between cardiovascular and postural control systems during standing before and after mild exercise. Significant cardiopostural input to blood pressure regulation was shown, suggesting the importance of cardiopostural integration when investigating orthostatic hypotension. In addition, we observed a reduction of baroreflex-mediated blood pressure regulation after exercise.

Systolic time intervals have been used to detect and quantify the directional changes of left ventricular function. New methods of recording these cardiac timings, which are less cumbersome, have been recently developed and this has created a renewed interest and novel applications for these cardiac timings. This manuscript reviews these new methods and addresses the potential for the application of these cardiac timings for the diagnosis and prognosis of different cardiac diseases.