Remote Speech Analysis in the Evaluation of Hospitalized Patients With Acute Decompensated Heart Failure.

OBJECTIVES: This study assessed the performance of an automated speech analysis technology in detecting pulmonary fluid overload in patients with acute decompensated heart failure (ADHF). BACKGROUND: Pulmonary edema is the main cause of heart failure (HF)-related hospitalizations and a key predictor of poor postdischarge prognosis. Frequent monitoring is often recommended, but signs of decompensation are often missed. Voice and sound analysis technologies have been shown to successfully identify clinical conditions that affect vocal cord vibration mechanics. METHODS: Adult patients with ADHF (n = 40) recorded 5 sentences, in 1 of 3 languages, using HearO, a proprietary speech processing and analysis application, upon admission (wet) to and discharge (dry) from the hospital. Recordings were analyzed for 5 distinct speech measures (SMs), each a distinct time, frequency resolution, and linear versus perceptual (ear) model; mean change from baseline SMs was calculated. RESULTS: In total, 1,484 recordings were analyzed. Discharge recordings were successfully tagged as distinctly different from baseline (wet) in 94% of cases, with distinct differences shown for all 5 SMs in 87.5% of cases. The largest change from baseline was documented for SM2 (218%). Unsupervised, blinded clustering of untagged admission and discharge recordings of 9 patients was further demonstrated for all 5 SMs. CONCLUSIONS: Automated speech analysis technology can identify voice alterations reflective of HF status. This platform is expected to provide a valuable contribution to in-person and remote follow-up of patients with HF, by alerting to imminent deterioration, thereby reducing hospitalization rates. (Clinical Evaluation of Cordio App in Adult Patients With CHF; NCT03266029).

Feasibility of remote speech analysis in evaluation of dynamic fluid overload in heart failure patients undergoing haemodialysis treatment.

AIMS: This study aimed to assess the ability of a voice analysis application to discriminate between wet and dry states in chronic heart failure (CHF) patients undergoing regular scheduled haemodialysis treatment due to volume overload as a result of their chronic renal failure. METHODS AND RESULTS: In this single-centre, observational study, five patients with CHF, peripheral oedema of ≥2, and pulmonary congestion-related dyspnoea, undergoing haemodialysis three times per week, recorded five sentences into a standard smartphone/tablet before and after haemodialysis. Recordings were provided that same noon/early evening and the next morning and evening. Patient weight was measured at the hospital before and after each haemodialysis session. Recordings were analysed by a smartphone application (app) algorithm, to compare speech measures (SMs) of utterances collected over time. On average, patients provided recordings throughout 25.8 ± 3.9 dialysis treatment cycles, resulting in a total of 472 recordings. Weight changes of 1.95 ± 0.64 kg were documented during cycles. Median baseline SM prior to dialysis was 0.87 ± 0.17, and rose to 1.07 ± 0.15 following the end of the dialysis session, at noon (P = 0.0355), and remained at a similar level until the following morning (P = 0.007). By the evening of the day following dialysis, SMs returned to baseline levels (0.88 ± 0.19). Changes in patient weight immediately after dialysis positively correlated with SM changes, with the strongest correlation measured the evening of the dialysis day [slope: -0.40 ± 0.15 (95% confidence interval: -0.71 to -0.10), P = 0.0096]. CONCLUSIONS: The fluid-controlled haemodialysis model demonstrated the ability of the app algorithm to identify cyclic changes in SMs, which reflected bodily fluid levels. The voice analysis platform bears considerable potential as a harbinger of impending fluid overload in a range of clinical scenarios, which will enhance monitoring and triage efforts, ultimately optimizing remote CHF management.

Automatic detection and classification of sleep stages by multichannel EEG signal modeling.

In this paper a novel method for automatic detection and classification of sleep stages using a multichannel electroencephalography (EEG) is presented. Understanding the sleep mechanism is vital for diagnosis and treatment of sleep disorders. The EEG is one of the most important tools of studying and diagnosing sleep disorders. EEG signals waveforms activity interpretation is performed by visual analysis (a very difficult procedure). This research aim is to ease the difficulties involved in the existing manual process of EEG interpretation by proposing an automatic sleep stage detection and classification system. The suggested method based on Multichannel Auto Regressive (MAR) model. The multichannel analysis approach incorporates the cross correlation information existing between different EEG signals. In the training phase, we used the vector quantization (VQ) algorithm, Linde-Buzo-Gray (LBG) and sleep stage definition, by estimation of probability mass functions (pmf) per every sleep stage using Generalized Log Likelihood Ratio (GLLR) distortion. The classification phase was performed using Kullback-Leibler (KL) divergence. The results of this research are promising with classification accuracy rate of 93.2%. The results encourage continuation of this research in the sleep field and in other biomedical signals applications.