A Remote Patient Monitoring System for Congestive Heart Failure

Suh, Myung-kyung; Chen, Chien-An; Woodbridge, Jonathan; Tu, Manghui; Kim, Jung In; Nahapetian, Ani; Evangelista, Lorraine S.; Sarrafzadeh, Majid

doi:10.1007/s10916-011-9733-y

Cited by 130 publications

(97 citation statements)

References 34 publications

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“…The system consists of a smartphone-based data collection gateway, an Internet-scale data storage and search system, and a backend analytics engine for diagnostic and prognostic purposes. A detailed description of WANDA-B is described elsewhere [6]. The purpose of WANDA-B is to capture symptoms that are difficult for a patient to report and changes in condition that evolve slowly over time.…”

Section: Methodsmentioning

confidence: 99%

Predicting adherence to use of remote health monitoring systems in a cohort of patients with chronic heart failure

Evangelista

Ghasemzadeh

Lee

et al. 2017

THC

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Background It is unclear whether subgroups of patients may benefit from remote monitoring systems (RMS) and what user characteristics and contextual factors determine effective use of RMS in patients with heart failure (HF). Objective The study was conducted to determine whether certain user characteristics (i.e. personal and clinical variables) predict use of RMS using advanced machine learning software algorithms in patients with HF. Methods This pilot study was a single-arm experimental study with a pre- (baseline) and post- (3 months) design; data from the baseline measures were used for the current data analyses. Sixteen patients provided consent; only 7 patients (mean age 65.8±6.1, range 58–83) accessed the RMS and transmitted daily data (e.g. weight, blood pressure) as instructed during the 12 week study duration. Results Baseline demographic and clinical characteristics of users and non-users were comparable for a majority of factors. However, users were more likely to have no HF specialty based care or an automatic internal cardioverter defibrillator. The precision accuracy of decision tree, multilayer perceptron (MLP) and k-Nearest Neighbor (k-NN) classifiers for predicting access to RMS was 87.5%, 90.3%, and 94.5% respectively. Conclusion Our preliminary data show that a small set of baseline attributes is sufficient to predict subgroups of patients who had a higher likelihood of using RMS. While our findings shed light on potential end-users more likely to benefit from RMS-based interventions, additional research in a larger sample is warranted to explicate the impact of user characteristics on actual use of these technologies.

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Section: Methodsmentioning

confidence: 99%

Predicting adherence to use of remote health monitoring systems in a cohort of patients with chronic heart failure

Evangelista

Ghasemzadeh

Lee

et al. 2017

THC

Self Cite

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“…Recently, considerable industrial and academic interest is received by the use of realization of wireless-enabled low-power ECG monitors. The most important things to see of this research and progress efforts are: (1) Toumaz's Sensium Life Pebble TZ203082 [2], a very-small and very-low-power observer for heart rate, 5 days on a hearing aid battery; (2) Intel's ShimmerTM [3], a small wireless wearable sensor platform able to record and wirelessly transmit 3-lead ECG data as well as accelerometer, gyroscope and galvanic skin response information; (3) a minute wireless wearable sensor stand able to record and wirelessly pass on 3-lead ECG data as well as accelerometer, gyroscope and galvanic skin response information; (3) IMEC's wireless solo-lead bipolar ECG patch [4] used for ambulatory monitoring claiming over 10 days of monitoring on a 160 mAh Li-ion battery (for hidden use conditions). The medical relevance of the primary structure is still being validated, as Toumaz aims to get extra than the system's so far established exact measurement of heart rate.…”

Section: Fig:1 Ecg Signalmentioning

confidence: 99%

Overview a Quality-Scalable and Energy-Efficient Approach for Spectral Analysis of Heart Rate Variability

Malviya¹,

Gupta²,

Sharma³

2015

IJCA

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In this paper presenting bioelectrical signals which are spectrally analyzed for enabling energy-quality trade-offs, they are helpful in observing different health problems as those related with the rate of heart. To facilitate such tradeoffs, the signals which are processed earlier are expressed primarily in a beginning in which considerable components that hold mainly of the related information can be simply notable from the components that effect the output to a smaller amount. Such an arrangement permits the pruning of operations allied with the less important signal components primary to power savings with loss of minor quality as simply less useful parts are reduced under the certain requirements. This provides the patients normal and abnormalities using ECG waves.

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“…In an attempt to handle the need and costs for continuous, portable monitoring of such disorders, Wireless Body Sensor Networks (WBSNs) have emerged in the last decade [2,3]. A typical WBSN comprises a set of wearable low-power nodes that record, monitor and wirelessly sent vital signals to a central network coordinator which could be another portable device, capable of analyzing the recorded signals.…”

Section: Introductionmentioning

confidence: 99%

A quality-scalable and energy-efficient approach for spectral analysis of heart rate variability

Karakonstantis¹,

Sankaranarayanan²,

Sabry³

et al. 2014

Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 2014

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Abstract-Today there is a growing interest in the integration of health monitoring applications in portable devices necessitating the development of methods that improve the energy efficiency of such systems. In this paper, we present a systematic approach that enables energy-quality trade-offs in spectral analysis systems for bio-signals, which are useful in monitoring various health conditions as those associated with the heart-rate. To enable such trade-offs, the processed signals are expressed initially in a basis in which significant components that carry most of the relevant information can be easily distinguished from the parts that influence the output to a lesser extent. Such a classification allows the pruning of operations associated with the less significant signal components leading to power savings with minor quality loss since only less useful parts are pruned under the given requirements. To exploit the attributes of the modified spectral analysis system, thresholding rules are determined and adopted at design-and run-time, allowing the static or dynamic pruning of less-useful operations based on the accuracy and energy requirements. The proposed algorithm is implemented on a typical sensor node simulator and results show up-to 82% energy savings when static pruning is combined with voltage and frequency scaling, compared to the conventional algorithm in which such trade-offs were not available. In addition, experiments with numerous cardiac samples of various patients show that such energy savings come with a 4.9% average accuracy loss, which does not affect the system detection capability of sinus-arrhythmia which was used as a test case.

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A Remote Patient Monitoring System for Congestive Heart Failure

Cited by 130 publications

References 34 publications

Predicting adherence to use of remote health monitoring systems in a cohort of patients with chronic heart failure

Predicting adherence to use of remote health monitoring systems in a cohort of patients with chronic heart failure

Overview a Quality-Scalable and Energy-Efficient Approach for Spectral Analysis of Heart Rate Variability

A quality-scalable and energy-efficient approach for spectral analysis of heart rate variability

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