A convolutional neural network approach to detect congestive heart failure

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal.Tracking the fluctuations in the blood glucose level is relevant for both healthy individuals and diabetic patients. High glucose levels (hyperglycemia) result in long-term complications and can damage the kidneys, nerves, blood vessels in the eye and can bring many other complications 14 . Low blood glucose levels (hypoglycemia) may result in acute short-term alterations of health status such as confusion, irritability, palpitations, feeling shaky and sweaty and can even result in severe loss of attention, coma or death 15 . In fact, hypoglycemia can be particularly dangerous during specific activities requiring great attention (e.g. while driving, performing complicated surgeries). Thus, technologies for non-invasive, continuous monitoring of glucose concentration aiming at early-detecting hypoglycemic events are highly required.The most diffuse methods for blood glucose testing consist of analysing a drop of blood resulted from a finger prick. However, this method does not allow continuous monitoring, is invasive, cumbersome, expensive, and it has been demonstrated that it affects patient compliance with the glucose measurements 16 . As an alternative, Continuous Glucose Monitoring Devices (CGMs) can infer the blood glucose levels in real-time based on the glucose in the interstitial fluid. These devices significantly empowered diabetic patients, but still, they present some limitations that make them unattractive for pre-diabetic patients and diabetics. Commercially available CGMs can be worn for a limited number of days, usually between 7 and 14 days. Most of the CGMs require finger prick calibration; some studies reported that the reliability of CGMs is limited 17-19 during low blood glucose level events and they sample from the interstitial fluid whi...

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“…The findings presented in this manuscript have been further investigated in a study by the same authors 79…”

Section: Discussionmentioning

confidence: 67%

Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG

Porumb

Stranges

Pescapè

et al. 2020

Sci Rep

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143

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“…In addition to the studies that use the detection of the typical heart sounds (S1, S2, S3), we were not able to find any studies on CHF using machine learning except for our previous work in this field [1], [53]. One very recent study on CHF detection was presented by Porumb et al [52], where they used CNNs on data collected with ECG devices. Our work differs from theirs, as we used PCG.…”

Section: Discussionmentioning

confidence: 98%

Machine Learning and End-to-End Deep Learning for the Detection of Chronic Heart Failure From Heart Sounds

et al. 2020

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Chronic heart failure (CHF) affects over 26 million of people worldwide, and its incidence is increasing by 2% annually. Despite the significant burden that CHF poses and despite the ubiquity of sensors in our lives, methods for automatically detecting CHF are surprisingly scarce, even in the research community. We present a method for CHF detection based on heart sounds. The method combines classic Machine-Learning (ML) and end-to-end Deep Learning (DL). The classic ML learns from expert features, and the DL learns from a spectro-temporal representation of the signal. The method was evaluated on recordings from 947 subjects from six publicly available datasets and one CHF dataset that was collected for this study. Using the same evaluation method as a recent PhysoNet challenge, the proposed method achieved a score of 89.3, which is 9.1 higher than the challenge's baseline method. The method's aggregated accuracy is 92.9% (error of 7.1%); while the experimental results are not directly comparable, this error rate is relatively close to the percentage of recordings labeled as ''unknown'' by experts (9.7%). Finally, we identified 15 expert features that are useful for building ML models to differentiate between CHF phases (i.e., in the decompensated phase during hospitalization and in the recompensated phase) with an accuracy of 93.2%. The proposed method shows promising results both for the distinction of recordings between healthy subjects and patients and for the detection of different CHF phases. This may lead to the easier identification of new CHF patients and the development of home-based CHF monitors for avoiding hospitalizations. INDEX TERMS Chronic heart failure, deep learning, heart sounds, machine learning, PCG.

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“…None of the patients did not take fingolimod used to treat RRMS. Fingolimod is known to reduce cardiac autonomic modulation (HR reduction) and baroreflex sensitivity at rest, as well as to diminish cardiovagal responses to autonomic challenges [ 54 ] Finally, researches should consider using machine learning approaches for clinical diagnosis based on physiological cardiac data [ 55 ].…”

Section: Discussionmentioning

confidence: 99%

Cardiac Autonomic Modulation Is Different in Terms of Clinical Variant of Multiple Sclerosis

Zawadka‐Kunikowska

Rzepiński²,

Newton

et al. 2020

JCM

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This study evaluates whether the cardiac autonomic response to head-up tilt test (HUTT) differs between patients with relapsing-remitting multiple sclerosis (RRMS) and those with progressive MS (PMS) as compared to healthy controls (HC). Baroreflex sensitivity, cardiac parameters, heart rate (HRV) and blood pressure variability (BPV) were compared between 28 RRMS, 21PMS and 25 HC during HUTT. At rest, PMS patients had higher values of the sympathovagal ratio, a low-frequency band HRV (LFnu-RRI) and lower values of parasympathetic parameters (HFnu-RRI, HF-RRI) compared to RRMS and HC. Resting values of cardiac parameters were significantly lower in RRMS compared to PMS patients. No intergroup differences were observed for post-tilt cardiac and autonomic parameters, except for delta HF-RRI with lower values in the PMS group. The MS variant corrected for age, sex and Expanded Disability Status Scale (EDSS) score was an independent predictor of changes in the sympathovagal ratio as measured by HRV. Furthermore, a higher overall EDDS score was related to a higher sympathovagal ratio, lower parasympathetic parameters at rest, and decrease post-tilt changes of the sympathovagal ratio of sBP BPV. Autonomic imbalance is markedly altered in the MS patient group compared to control changes were most pronounced in the progressive variant of MS disease. The MS variant appeared to have a potential influence on cardiac autonomic imbalance at rest.

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A convolutional neural network approach to detect congestive heart failure

Cited by 92 publications

References 42 publications

Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG

Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG

Machine Learning and End-to-End Deep Learning for the Detection of Chronic Heart Failure From Heart Sounds

Cardiac Autonomic Modulation Is Different in Terms of Clinical Variant of Multiple Sclerosis

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