Journal of Gerontological Nursing

Feature Article 

Using Sensor Signals in the Early Detection of Heart Failure: A Case Study

Laurel A. Despins, PhD, RN; Giovanna Guidoboni, PhD; Marjorie Skubic, PhD; Lorenzo Sala, PhD; Moein Enayati, PhD; Mihail Popescu, PhD; Chelsea B. Deroche, PhD

Abstract

Early detection of heart failure in older adults will be a significant issue for the foreseeable future. The current article presents a case study to describe how monitoring ballistocardiogram (BCG) waveforms captured non-invasively using sensors placed under a bed mattress can detect early heart failure changes. Heart and respiratory rates obtained from the bed sensor of a female older adult who was hospitalized with acute mixed congestive heart failure, clinic notes, and data from computer simulations reflecting increasing diastolic dysfunction were analyzed. Mean heart and respiratory rate trends obtained from her bed sensor in the prior 2 months did not indicate heart failure. BCG waveforms resulting from the simulations demonstrated changes associated with decreasing cardiac output as diastolic function worsened. Developing new methods for clinically interpreting BCG waveforms presents a significant opportunity for improving early heart failure detection. [Journal of Gerontological Nursing, 46(7), 41–46.]

Abstract

Early detection of heart failure in older adults will be a significant issue for the foreseeable future. The current article presents a case study to describe how monitoring ballistocardiogram (BCG) waveforms captured non-invasively using sensors placed under a bed mattress can detect early heart failure changes. Heart and respiratory rates obtained from the bed sensor of a female older adult who was hospitalized with acute mixed congestive heart failure, clinic notes, and data from computer simulations reflecting increasing diastolic dysfunction were analyzed. Mean heart and respiratory rate trends obtained from her bed sensor in the prior 2 months did not indicate heart failure. BCG waveforms resulting from the simulations demonstrated changes associated with decreasing cardiac output as diastolic function worsened. Developing new methods for clinically interpreting BCG waveforms presents a significant opportunity for improving early heart failure detection. [Journal of Gerontological Nursing, 46(7), 41–46.]

Early detection of heart failure in older adults will be a significant issue for the foreseeable future. The number of Americans age 65 and older with heart failure is expected to be more than 6 million by 2030, an increase of 72% from 2012 (Heidenreich et al., 2013). Hospital readmissions for heart failure are associated with $1.7 billion in total costs (Hines et al., 2014). Chronic heart failure decreases physical functioning and the ability to engage in and complete tasks, adversely impacting an individual's health-related quality of life (Auld et al., 2018; Toukhsati et al., 2015). Early detection of progressive deterioration in cardiac function can provide nurses with an opportunity to quickly engage with individuals and/or their caregivers to assess current self-management activities and functional ability and intervene where appropriate to maintain and/or optimize individuals' independence.

Currently, early detection of worsening heart failure relies heavily on the individual's self-monitoring of weight and symptoms, which is often inconsistent (Albert et al., 2014; Toukhsati et al., 2015). Data from in-home sensor technology are effective in the early detection of acute health events, such as urinary tract infections (Rantz et al., 2011) and falls (Feldwieser et al., 2014), and hold promise for detecting changes associated with chronic conditions such as heart failure. Trends in heart and respiratory rates often herald health deterioration (Churpek et al., 2016). Sensor technology can currently provide in-home monitoring of heart and respiratory rates to enhance the detection of worsening heart failure (Inan et al., 2015). Advances in sensor signal analyses may enable earlier detection of declining cardiac function before the development of heart failure.

We present a case study of a female older adult with a history of hyper-tension and no previous diagnosis of heart failure who was hospitalized with acute mixed congestive heart failure. She had a sensor under her bed mattress at home that provided heart and respiratory rates.

Background

The rapid onset of signs and symptoms of heart failure is the most common cause of unplanned hospitalization in western nations. Older adults are more likely to have an atypical clinical presentation of acute heart failure with the predominant symptoms being fatigue, confusion, and dyspnea (Teixeira et al., 2016). Approximately 50% of adults with heart failure have diastolic dysfunction with preserved ejection fraction (i.e., ≥50%). The risk of developing heart failure with preserved ejection fraction increases sharply with age (Dunlay et al., 2017). As the body ages, the myocardium stiffens, decreasing compliance and altering left ventricular diastolic filling. During times of stress, such as myocardial ischemia or atrial fibrillation with rapid ventricular response, cardiac filling may be reduced (Dunlay et al., 2017). More females than males are diagnosed with heart failure with preserved ejection fraction, and most adults with this type of heart failure have a history of hypertension (Dunlay et al., 2017). There is no single diagnostic test that adequately diagnoses heart failure with preserved ejection fraction, and early detection is difficult (Dunlay et al., 2017). The most common symptom is exertional dyspnea; yet, in adults age ≥65, this symptom could reflect normal aging-related physiological factors or other non-cardiac etiologies (Upadhya & Kitzman, 2017).

In-Home Sensors

An important function of sensor technology in the homes of older adults is the detection of pattern changes indicative of possible health changes in time-series data (Choi et al., 2019). Sensors can be wearable. Flexible sensors can be incorporated into textile fibers, clothes, and bands, or attached to the body. They can capture real-time heart and respiratory rates, blood pressure, arterial oxygen saturation, and electrocardiograms (Majumder et al., 2017). Sensors can also be embedded in the living environment, including passive motion sensors, wireless bioimpedance scales, and bed sensors. These sensors provide physiological data including weight, pulse wave velocity, and heart and respiratory rates (Kaye et al., 2018).

Ballistocardiograms

The ejection of blood into the great vessels from ventricular contraction displaces the body's center of mass. Ballistocardiogram (BCG) signals are sequences of waves denoting the body's repetitive motion resulting from the ejection of blood with each heartbeat. Information can be obtained regarding the motion of the body's center of mass (displacement, velocity, and acceleration) due to the circulation of blood. The motion of the body's center of mass reflects the function of the cardiovascular system (Starr & Noordergraaf, 1967). BCG signals can be captured non-invasively by sensing devices placed in chairs; under pillows, mattresses, and bed frames; or as part of weight scales. Software algorithms are used to analyze the signal and extract heart and respiratory rate information (Inan et al., 2015).

Case Study

A 94-year-old woman with a medical history of hypertension and a new diagnosis of acute mixed congestive heart failure was chosen to provide insight into the potential future role of monitoring BCG signals as a means of detecting early heart failure. The woman and her husband lived at home and did not require assistance with independent activities of daily living.

The woman presented to her physician's office on February 15, 2018, with complaints of shortness of breath for 3 days and no energy. She was found to have atrial fibrillation with a rapid ventricular response up to 150 beats per minute (BPM), jugular vein distention, and crackles two thirds the way up her lung fields. Her blood pressure was 166/82 mmHg with a mean arterial pressure of 110 mmHg, peripheral oxygen saturation on room air was 89%, and her weight was 50 kg. Her most recent previous visit had been on February 6, 2018, as a follow up for a complaint of back pain. Her heart rate was regular at 61 BPM, blood pressure was 118/56 mmHg with a mean arterial pressure of 77 mmHg, oxygen saturation on room air of 93%, and weight of 48 kg. The patient had been on antihypertension medication, which had been discontinued for a period of several months and restarted 2 weeks prior to the February 15 physician visit. The patient was transported to a local hospital where she was treated for 1 week for acute mixed congestive heart failure, discharged to a skilled nursing facility for cardiac rehabilitation, and died 1 week later.

Data Collection

The patient had a sensor under her mattress that captured BCG signals when she was in bed. Nighttime hourly heart and respiratory rates were extracted from a database containing bed sensor BCG signals between December 14, 2017 and February 14, 2017, 2 months prior to her physician office visit on February 15, 2018. Boehmer et al. (2013) noted that patients with heart failure with higher 30-day variation in daily median respiratory rate trends had an approximate five-fold increased risk of hospitalization for heart failure within the next 1 month.

The bed sensor was placed under the mattress for a period of 1 year to collect bio-signals consisting of low-frequency respiratory-related motions and mid-frequency cardiovascular-related BCGs. The bed sensor system comprises four water tubes placed vertically, parallel with each other, underneath the mattress where the individual's upper torso lies. At the end of each water tube is a pressure sensor that converts the hydraulic pressure to an electrical signal representing the BCG (Rosales et al., 2017). If a daily mean heart or respiratory rate exceeds four standard deviations of the individual's overall mean, automated secure email health alerts are sent to a designated nurse if the individual lives in a senior living facility. For individuals living independently, health alerts are sent to the individual if they so choose, and/or designated others, such as family members. The woman's family member, who lived in another state, received the alerts but did not have access to the woman's medical records and did not have any clinical training. Blood pressure, heart rate, and respiratory rate data obtained in the patient's primary care clinic between August 15, 2017 and February 14, 2018, were abstracted from copies of clinic notes. Results from an echo-cardiogram performed on February 19, 2018, were abstracted from copies of hospital records.

BCG signals can be altered by changes in ventricular contractility (Pinheiro et al., 2010). As BCG signals are produced by the body's repetitive motion from blood ejected into the great vessels (Starr & Noordergraaf, 1967), we hypothesized that the morphology of the BCG waveform would alter as left ventricular diastolic stiffness increased (i.e., increased elastance). Increased elastance is a component of heart failure with preserved ejection fraction (Redfield, 2016). Using a closed-loop mathematical model of the cardiovascular system that proposes quantitative methods for clinically interpreting BCG signals (Guidoboni et al., 2019), we conducted computer simulations of three scenarios and compared the resulting theoretical BCG waveforms and cardiac indices. Scenario 1 was based on normal left ventricular elastance; scenario 2 reflected a 20% increase in left ventricular elastance; and scenario 3 reflected a 60% increase in left ventricular elastance.

Analysis

As the number of hourly observations varied from night to night depending on when the woman went to bed, arose in the morning, and got out of bed during the night, scatterplots were created. These plots indicated a possible change point approximately 1 month prior to hospitalization. An interrupted time series analysis was then conducted examining the time periods of 1 month, 3 weeks, 2 weeks, 1 week, and 3 days prior to hospitalization. Run charts of the mean arterial pressure, heart rate, and respiratory rate data from the clinic notes were created to visualize potential trends.

Results

Prior to hospitalization (December 5, 2017 to February 6, 2018), there were no trends in mean arterial pressures or heart and respiratory rates obtained during clinic visits. There was no significant change in daily minimum heart rate or minimum/maximum/mean respiratory rates obtained from the BCG signals. There was no significant change in daily mean or maximum heart rates obtained from the BCG signals until the woman began to experience symptoms 3 days before hospitalization when the mean daily heart rate increased by 16 BPM and the maximum daily heart rate increased by 35 BPM (Table 1). There were three health alerts for heart rates of 71 BPM, 75 BPM, and 61 BPM on December, 25, 2017; one alert for a heart rate of 69 BPM on January 15, 2018; three alerts for heart rates of 86 BPM, 87 BPM, and 86 BPM on February 11, 2018; and one alert for a heart rate of 76 BPM on February 12, 2018. The woman's family member communicated via telephone with the woman after receiving the alerts, but the woman only reported that she was “tired” and would “go lie down for a bit.” An echocardiogram during the hospitalization revealed a normalsized left ventricle with moderate concentric left ventricular hypertrophy, an estimated ejection fraction 55% to 60%, elevated left ventricular filling pressures, a severely dilated left atrium, a moderate to severely dilated right atrium, trivial mitral valve regurgitation, and moderate tricuspid valve regurgitation.

Parameter Estimates for Daily Mean and Daily Maximum Heart Rates from Interrupted Time Series Models of Bed Sensor Data

Table 1:

Parameter Estimates for Daily Mean and Daily Maximum Heart Rates from Interrupted Time Series Models of Bed Sensor Data

The theoretical waveforms generated by the computer simulations illustrated decreases in center of mass displacement, velocity, and acceleration as left ventricular elastance increased (Figure 1). The resulting cardiac parameters demonstrated decreasing left ventricular end-diastolic volumes, endsystolic volumes, stroke volumes, and cardiac outputs as elastance increased (Table 2). Similar decreases are seen with worsening left ventricular diastolic dysfunction (Redfield, 2016).

Ballistocardiogram (BCG) waveforms of increasing left ventricular elastance simulation.

Figure 1.

Ballistocardiogram (BCG) waveforms of increasing left ventricular elastance simulation.

Left Ventricle Simulation Cardiac Indices Results

Table 2:

Left Ventricle Simulation Cardiac Indices Results

Discussion

Current monitoring using sensors focuses on identifying functional decline and pattern changes to detect early illness or chronic condition exacerbation onset (Choi et al., 2019). Findings of the current case study illustrate the limitations in monitoring heart and respiratory rate trends for cues indicative of health deterioration. The patient's trends did not significantly change until 3 days prior to hospitalization. During these 3 days, she experienced symptoms of fatigue and shortness of breath prompting her to see her physician. Health alerts were automatically sent during this time period but due to the distance between the family member and the patient, the family member was unable to visit the patient and, not having clinical training, had to rely on the patient's report of symptoms during telephone communication. Health alerts reflect pattern changes potentially indicating a health event. As illustrated by this case, it is necessary to coordinate real-time health data with clinician support to optimize early detection of a new health event. Such coordination prompts focused assessment and closer monitoring of the individual with the goal of early intervention to restore health (Rantz et al., 2015). However, new approaches are needed to detect more gradual worsening of cardiac function associated with chronic conditions, such as hypertension, to prevent or delay the onset of new conditions, such as heart failure.

BCG signals offer opportunities to detect changes in cardiac function beyond monitoring heart and respiratory rates. Researchers have been able to use BCG signals to measure ventricular contractility, diastolic filling times, and stroke volume (Ashouri et al., 2016; Inan et al., 2009). As seen with our theoretical BCG waveforms resulting from the computer simulations (Figure 1), developing methods for analyzing BCG waveform changes presents a significant opportunity for detecting subtle changes in cardiac function that long precede changes in heart and respiratory rate trends. Detection of such changes optimizes early implementation of interventions that avoid or slow the progression of deteriorating cardiac function. Optimizing cardiac function through early interventions helps maintain older adults' functioning and independence as well as decrease unplanned hospitalizations and other health care utilization (Abraham et al., 2011).

Research to enhance and standardize the identification of BCG waveform features is ongoing. Future research will include the development of an alert system to notify individuals and/or their caregivers and health care team members when BCG waveform indications of deterioration are present and a clinical decision support system to guide actions in light of this information. In the future, nurses can encourage individuals at risk for developing cardiovascular deterioration (e.g., those with hypertension) to implement environmental sensors that can generate BCG signals to passively monitor their cardiovascular system over time. Armed with earlier information concerning deterioration, nurses can collaborate with the individual and/or their caregiver to co-create a plan for further in-home monitoring, such as weekly blood pressure measurements, and establish criteria regarding when to contact the health care team.

Conclusion

The case study patient's heart and respiratory rate trends did not indicate the presence of cardiac dysfunction until 3 days prior to hospitalization. Remote monitoring of heart and respiratory rates has limitations in the early detection of cardiac dysfunction, such as heart failure. Monitoring for changes in the BCG waveform over time can provide opportunities for capturing cardiac changes indicative of health deterioration earlier and intervening sooner to avoid or delay the onset of heart failure.

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Parameter Estimates for Daily Mean and Daily Maximum Heart Rates from Interrupted Time Series Models of Bed Sensor Data

Change PointIntercept (β0)Pre-Trend (β1)Post-Level Change (β2)p Value
Mean Daily Heart Rate
None57.82730.0320
1 month56.11940.12322.81800.137
3 weeks56.29300.11710.19230.919
2 weeks56.49220.1037−1.73410.394
1 week56.95320.0767−4.92060.08
3 days57.84700.031416.35360.0007
Maximum Daily Heart Rate
None61.96900.1361
1 month61.14770.17811.68070.663
3 weeks61.43700.16400.50690.894
2 weeks61.01360.1891−3.35560.412
1 week61.64120.1528−3.11100.581
3 days62.48510.110535.48950.0001

Left Ventricle Simulation Cardiac Indices Results

End Diastolic Volume (mL)End Systolic Volume (mL)Stroke Volume (mL/beat)Cardiac Output (L/min)
Baseline155.6567.488.266.62
20% increase in left ventricular elastance144.761.2783.436.26
60% increase in left ventricular elastance126.8151.2675.555.67
Authors

Dr. Despins is Assistant Professor and Dr. Deroche is Assistant Professor, Sinclair School of Nursing, Dr. Guidoboni is Professor and Dr. Skubic is Professor, Electrical Engineering & Computer Science, and Dr. Popescu is Professor and Dr. Deroche is also Assistant Professor, Health Management and Informatics, University of Missouri, Columbia, Missouri; Dr. Sala is Research Associate, Department of Mathematics, Imperial College London, London, United Kingdom; and Dr. Enayati is Research Associate, Mayo Clinic, Rochester, Minnesota. Dr. Guidoboni is also Professor, Department of Mathematics, Dr. Skubic is also Director, Center for Eldercare & Rehabilitation Technology, University of Missouri, Columbia, Missouri.

Dr. Guidoboni has a patent Model-Based Sensor Technology for Detection of Cardiovascular Status pending. Dr. Skubic reports small ownership in Foresite Healthcare, outside the submitted work. In addition, Dr. Skubic has a patent Integrated Sensor Network Methods and Systems licensed to Foresite Healthcare, and a patent Hydraulic Bed Sensor and System for Non-Invasive Monitoring of Physiological Data, Status pending, licensed to Foresite Healthcare. All remaining authors have disclosed no potential conflicts of interest, financial or otherwise.

The authors thank Dr. James Keller for his expertise and input during the course of the study.

Address correspondence to Laurel A. Despins, PhD, RN, Assistant Professor, Sinclair School of Nursing, University of Missouri, S422 School of Nursing, Columbia, MO 65211-6000; email: despinsl@missouri.edu.

10.3928/00989134-20200605-07

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