Identification of the Vibrations Corresponding with Heart Sounds using Vibrational Cardiography.

Cardiography enables diagnostic and preventive care in hospitals and outpatient scenarios. However, most heart monitors do not distinguish the phases of the cardiac cycle. The transition between phases is indicated by the primary heart sounds. OBJECTIVE: Automatically identify the vibrations corresponding to both heart sounds. METHODS: Cardiac activity was monitored for 15 subjects while at rest, during exertion, and while performing static breath holds. The subjects consisted of 6 males and 9 females between the ages of 18-39 years with no known cardiorespiratory ailments. Motion corresponding to the heart sounds was identified using vibrational cardiography (VCG). The waveforms were processed to obtain quantities associated with their linear jerk and rotational kinetic energy. RESULTS: The ability to identity the first vibration was evaluated using the heart rate as a figure of merit. Its correlation with electrocardiography (ECG) measurements produced a r2 coefficient of 0.9887. The second vibration was compared with impedance cardiography (ICG) based on its delay from the ECG R-peak, and the fraction of the beat duration occupied by left ventricular ejection time. The comparisons produced r2 values of 0.251 and 0.2797, respectively. CONCLUSION: The vibrations corresponding to both primary heart sounds have the potential to be analyzed using VCG. SIGNIFICANCE: This study provides evidence of the feasibility of using VCG in identifying mechanical cardiovascular function. It facilitates non-invasive cardiac health monitoring in daily life.

Neural Network-based Classification of Static Lung Volume States using Vibrational Cardiography.

Non-invasive health monitoring has the potential to improve the delivery and efficiency of medical treatment. OBJECTIVE: This study was aimed at developing a neural network to classify the lung volume state of a subject (i.e. high lung volume (HLV) or low lung volume (LLV), where the subject had fully inhaled or exhaled, respectively) by analyzing cardiac cycles extracted from vibrational cardiography (VCG) signals. METHODS: A total of 15619 cardiac cycles were recorded from 50 subjects, of which 9989 cycles were recorded in the HLV state and the remaining 5630 cycles were recorded in the LLV state. A 1D convolutional neural network (CNN) was employed to classify the lung volume state of these cardiac cycles. RESULTS: The CNN model was evaluated using a train/test split of 80/20 on the data. The developed model was able to correctly classify the lung volume state of 99.4% of the testing data. CONCLUSION: VCG cardiac cycles can be classified based on lung volume state using a CNN. SIGNIFICANCE: These results provide evidence of a correlation between VCG and respiration volume, which could inform further analysis into VCG-based cardio-respiratory monitoring.

Analyzing Heart Rate Estimation from Vibrational Cardiography with Different Orientations.

Remote health monitoring is a widely discussed topic due to its potential to improve quality and delivery of medical treatment and the global increase in cardiovascular diseases. OBJECTIVE: Seismocardiography and Gyrocardiography have been shown to provide reliable heart rate information. A simple and efficient setup was developed for the monitoring of mechanical signals at the sternum. An algorithm based in autocorrelation was run on subjects with different orientations in order to detect heart rate. METHODS: Subjects performed several tests where both SCG and GCG were recorded using an inertial measurement unit, a Raspberry Pi and a BIOPAC acquisition system. A total of 2335 cardiac cycles were obtained from 5 subjects. Heart rate was determined on a per second basis and compared with an electrocardiography (ECG) reference by correlation coefficients. Ensemble averages were used to visualize differences in VCG morphology. RESULTS: Heart rate estimation obtained from VCG signals across all 5 subjects was referenced with ECG and achieved an r-squared correlation coefficient of 0.956 when supine and 0.975 when standing, compared to 0.965 across the entire dataset. CONCLUSION: Autocorrelated Differential Algorithm was able to successfully detect heart rate, regardless of orientation and posture. SIGNIFICANCE: Changes in orientation of the body during measurement introduce inaccuracies. This work shows that the algorithm is resistant to orientation and more adaptable to everyday life.

Relationship of the Respiration Waveform to a Chest Worn Inertial Sensor.

Demand of portable health monitoring has been growing due to increasing cardiovascular and respiratory diseases. While both cardiovascular monitoring and respiratory monitoring have been developed independently, there lacks a simple integrated solution to monitor both simultaneously. Seismocardiography (SCG), a method of recording cardiac vibrations with an accelerometer can also be used to extract respiratory information via low frequency chest oscillations. This study used an inertial measurement unit which pairs a 3-axis accelerometer and a 3-axis gyroscope to monitor respiration while maintaining optimum placement protocol for recording SCG. Additionally, the connection between inertial measurement and both respiratory rate and volume were explored based on their correlation with a Spirometer. Respiratory volume was shown to have moderate correlation with chest motion with an average best-case correlation coefficient of 0.679 across acceleration and gyration. The techniques described will assist the design of future SCG algorithms by understanding the sources behind their modulation from respiration. This paper shows that a simplified processing technique can be added to SCG algorithms for respiration monitoring.

Real-Time Cardiac Beat Detection and Heart Rate Monitoring from Combined Seismocardiography and Gyrocardiography.

Cardiography is an indispensable element of health care. However, the accessibility of at-home cardiac monitoring is limited by device complexity, accuracy, and cost. We have developed a real-time algorithm for heart rate monitoring and beat detection implemented in a custom-built, affordable system. These measurements were processed from seismocardiography (SCG) and gyrocardiography (GCG) signals recorded at the sternum, with concurrent electrocardiography (ECG) used as a reference. Our system demonstrated the feasibility of non-invasive electro-mechanical cardiac monitoring on supine, stationary subjects at a cost of $100, and with the SCG-GCG and ECG algorithms decoupled as standalone measurements. Testing was performed on 25 subjects in the supine position when relaxed, and when recovering from physical exercise, to record 23,984 cardiac cycles at heart rates in the range of 36-140 bpm. The correlation between the two measurements had r2 coefficients of 0.9783 and 0.9982 for normal (averaged) and instantaneous (beat identification) heart rates, respectively. At a sampling frequency of 250 Hz, the average computational time required was 0.088 s per measurement cycle, indicating the maximum refresh rate. A combined SCG and GCG measurement was found to improve accuracy due to fundamentally different noise rejection criteria in the mutually orthogonal signals. The speed, accuracy, and simplicity of our system validated its potential as a real-time, non-invasive, and affordable solution for outpatient cardiac monitoring in situations with negligible motion artifact.

Effect of Static Respiratory Volume on the Waveform of Cardiac-induced Sternal Vibrations.

Cardio-respiratory activity originating in the chest creates vibrations that diffuse through the organs to the thoracic wall. The vibrational waves were detected in all six degrees of freedom by an inertial motion sensor at the xiphoid process of the sternum. Vibrational cardiography (VCG) combines the detection of vibrations via acceleration, termed as seismocardiography, and gyration, termed as gyrocardiography. The objective of this study was to determine the effect of static respiration volume on the morphology of cardiac-induced waveforms in the VCG signal. In this study, 24 subjects were tested while holding breath at peak inhalation, and at peak exhalation. Ensemble averages of the waveforms showed larger variations in the signal when the lungs were inhaled for both the primary and secondary heart sounds. Inter-subject variability was accounted for by averaging all waveforms and calculating the root mean squared value over a sliding window of 60 milliseconds. The peak amplitudes of both heart sounds were consistently larger for high lung volumes. However, the ratio of primary to the secondary heart sound was found to be inversely proportional to lung volume. These opposing effects offer a strong analysis tool for the determination of relative inhalation volume using VCG morphology alone.

Understanding the Impact of Electronic Medical Record Use on Practice-Based Population Health Management: A Mixed-Method Study.

BACKGROUND: Practice-based population health (PBPH) management is the proactive management of patients by their primary care clinical team. The ability of clinics to engage in PBPH and the means by which they incorporate it in a clinical setting remain unknown. OBJECTIVE: We conducted the Canadian Population Health Management Challenge to determine the capacity and preparedness of primary care settings to engage in PBPH using their existing medical record systems and to understand the complexities that may exist in PBPH implementation. METHODS: We recruited a sample of electronic medical record (EMR) -enabled and paper-based clinics from across Canada to participate in the challenge. The challenge required clinic staff and physicians to complete time-controlled, evidence-based practice reviews of their patients who may benefit from evidence-informed care, treatment, or interventions across five different areas (immunization, postmyocardial infarction care, cancer screening, diabetes management, and medication recall). We formulated a preparedness index to measure the capacity of clinics to engage in PBPH management. Finally, we conducted follow-up qualitative interviews to provide richer understanding of PBPH implementation and related issues (ie, challenges and facilitators). RESULTS: A total of 11 primary care clinics participated, representing 21 clinician practices. EMR-enabled clinics completed a full review of charts in an average of 1.37 hours. On the contrary, paper-based clinics reviewed nearly 10% of their charts in an average of 3.9 hours, hinting that they would have required an estimated 40 hours to complete a review of charts in their practice. Furthermore, the index revealed a major gap in preparedness between the EMR and paper-based clinics (0.86-3.78 vs 0.05-0.12), as well as a broad range among the EMR clinics. Finally, building on the results of the qualitative analysis, we identified factors facilitating the integration of PBPH. CONCLUSIONS: Our results suggest that EMR usage is pivotal in setting the foundation to support PBPH. The wide range of performance variation among EMR-enabled clinics suggests that EMR functionality and optimization, its support of clinical practice workflow, and policy issues to ensure adoption of standards are critical issues to facilitate PBPH.

Return on investment in electronic health records in primary care practices: a mixed-methods study.

BACKGROUND: The use of electronic health records (EHR) in clinical settings is considered pivotal to a patient-centered health care delivery system. However, uncertainty in cost recovery from EHR investments remains a significant concern in primary care practices. OBJECTIVE: Guided by the question of "When implemented in primary care practices, what will be the return on investment (ROI) from an EHR implementation?", the objectives of this study are two-fold: (1) to assess ROI from EHR in primary care practices and (2) to identify principal factors affecting the realization of positive ROI from EHR. We used a break-even point, that is, the time required to achieve cost recovery from an EHR investment, as an ROI indicator of an EHR investment. METHODS: Given the complexity exhibited by most EHR implementation projects, this study adopted a retrospective mixed-method research approach, particularly a multiphase study design approach. For this study, data were collected from community-based primary care clinics using EHR systems. RESULTS: We collected data from 17 primary care clinics using EHR systems. Our data show that the sampled primary care clinics recovered their EHR investments within an average period of 10 months (95% CI 6.2-17.4 months), seeing more patients with an average increase of 27% in the active-patients-to-clinician-FTE (full time equivalent) ratio and an average increase of 10% in the active-patients-to-clinical-support-staff-FTE ratio after an EHR implementation. Our analysis suggests, with a 95% confidence level, that the increase in the number of active patients (P=.006), the increase in the active-patients-to-clinician-FTE ratio (P<.001), and the increase in the clinic net revenue (P<.001) are positively associated with the EHR implementation, likely contributing substantially to an average break-even point of 10 months. CONCLUSIONS: We found that primary care clinics can realize a positive ROI with EHR. Our analysis of the variances in the time required to achieve cost recovery from EHR investments suggests that a positive ROI does not appear automatically upon implementing an EHR and that a clinic's ability to leverage EHR for process changes seems to play a role. Policies that provide support to help primary care practices successfully make EHR-enabled changes, such as support of clinic workflow optimization with an EHR system, could facilitate the realization of positive ROI from EHR in primary care practices.

Regulation of the rainbow trout (Oncorhynchus mykiss) hepatic beta2-adrenoceptor by adrenergic agonists.

The characteristics of hepatic beta(2)-adrenoceptors (AR) were examined in rainbow trout (Oncorhynchus mykiss) chased once per day to exhaustion for up to 7 days or fed the repartitioning agents clenbuterol (CLEN) or ractopamine (RACT) that function in mammals as beta-agonists. A one-day chase and feeding the CLEN for 37 days resulted in a significant 27% and 33% decrease, respectively, in the number of CGP-binding sites (B(max)) with no significant change in affinity (Kd) of hepatic beta(2)-ARs. Despite the significant decrease in beta(2)-AR numbers with CLEN feeding, no significant differences were found for either beta(2)-AR mRNA levels or adenylyl cyclase (ACase) activities. In addition, CLEN displayed only partial agonist activities as it was found to be more effective at blocking isoproterenol-stimulated cAMP production in isolated hepatocytes than stimulating cAMP production. The small affects of RACT may be related to its low active stereoisomer content and low affinity for the trout beta(2)-AR. Agonist regulation of the trout hepatic beta(2)-ARs may involve down-regulation of the receptors without affecting responsiveness.

The rainbow trout skeletal muscle beta-adrenergic system: characterization and signaling.

The presence and functionality of beta-adrenoceptors (beta-ARs) were examined in red (RM) and white muscle (WM) membranes isolated from the rainbow trout Oncorhynchus mykiss. Specific binding assays revealed the presence of a single class of binding sites with similar affinities in both muscle types (K(d) in nM: 0.14 +/- 0.03 and 0.18 +/- 0.03 for RM and WM, respectively) but with a significantly higher number of binding sites in RM compared with WM (B(max) in fmol/mg protein: 3.22 +/- 0.11 and 2.60 +/- 0.13, respectively). Selective and nonselective beta-adrenergic agonists (beta-AAs) and antagonists indicated an atypical beta-AR pharmacology. This result may represent a nonmammalian beta-AR classification or, more likely, the presence of more than one beta-AR subtype in trout muscles with similar affinities that could not be kinetically resolved. Adenylyl cyclase (ACase) assays showed a dose-dependent increase in cAMP production as concentrations of beta(2)-AAs increased in both muscle membranes with significantly higher basal cAMP production in RM compared with WM (cAMP production in pmol cAMP. mg protein(-1). 10 min(-1): 24.67 +/- 3.06 and 9.64 +/- 3.45, respectively). The agonist-induced increase in cAMP production was blocked by the beta-adrenergic antagonist propranolol, while the ACase activator forskolin increased cAMP production by 7- to 14-fold above basal and approximately 3-fold above all beta-AAs tested. This study demonstrated the presence of atypical beta(2)-ARs on RM and WM membranes of trout, suggesting that beta(2)-AAs may be a tool to enhance protein accretion through this signaling pathway.