Very few studies have measured disease penetrance and prognostic factors of Chagas cardiomyopathy among asymptomatic Trypanosoma cruzi-infected persons.We performed a retrospective cohort study among initially healthy blood donors with an index T cruzi-seropositive donation and age-, sex-, and period-matched seronegatives in 1996 to 2002 in the Brazilian cities of São Paulo and Montes Claros. In 2008 to 2010, all subjects underwent medical history, physical examination, ECGs, and echocardiograms. ECG and echocardiogram results were classified by blinded core laboratories, and records with abnormal results were reviewed by a blinded panel of 3 cardiologists who adjudicated the outcome of Chagas cardiomyopathy. Associations with Chagas cardiomyopathy were tested with multivariate logistic regression. Mean follow-up time between index donation and outcome assessment was 10.5 years for the seropositives and 11.1 years for the seronegatives. Among 499 T cruzi seropositives, 120 (24%) had definite Chagas cardiomyopathy, and among 488 T cruzi seronegatives, 24 (5%) had cardiomyopathy, for an incidence difference of 1.85 per 100 person-years attributable to T cruzi infection. Of the 120 seropositives classified as having Chagas cardiomyopathy, only 31 (26%) presented with ejection fraction <50%, and only 11 (9%) were classified as New York Heart Association class II or higher. Chagas cardiomyopathy was associated (P<0.01) with male sex, a history of abnormal ECG, and the presence of an S3 heart sound.There is a substantial annual incidence of Chagas cardiomyopathy among initially asymptomatic T cruzi-seropositive blood donors, although disease was mild at diagnosis.

BACKGROUND:

Low left ventricular ejection fraction (LVEF) and the presence of restrictive LV filling pattern are poor prognosticators in heart failure patients with reduced EF (HFREF). We sought to investigate whether acoustic cardiography can identify these high-risk HFREF subgroups.

METHODS:

A total of 127 HFREF patients (EF<50%) were enrolled into our study. All patients underwent acoustic cardiographic and echocardiographic examinations. Acoustic cardiographic parameters included S3 score (probability that the third heart sound exists), electromechanical activation time (EMAT, interval from Q wave to the first heart sound; %EMAT is the proportion of cardiac cycle that EMAT occupies), and systolic dysfunction index (SDI, a derived variable from the combination of %EMAT, S3 score, QRS duration and QR interval). Receiver operating characteristic curves were used to determine diagnostic utility of acoustic cardiography.

RESULTS:

SDI discriminated (area under curve [AUC], 0.79; 95% confidence interval [CI], 0.71-0.87) patients with severely impaired EF (EF≤35%) from those with moderately impaired EF (35%<EF<50%) with an SDI>5 that yielded 87% sensitivity and 60% specificity. An S3 score>4 identified patients with restrictive LV filling pattern with 0.76 AUC (95% CI, 0.67-0.84), 81% sensitivity and 55% specificity.

CONCLUSIONS:

SDI and S3 score obtained by acoustic cardiography identified HFREF patients with severely impaired systolic and diastolic function, respectively. This simple, bedside technology may be used as a screening tool to identify the sickest HFREF patients for more intensive therapy.

The feasibility of detecting heart sounds (HS) from an accelerometer sensor enclosed within an implantable cardioverter defibrillator (ICD) pulse generator (PG) was explored in a noninvasive pilot study on heart failure (HF) patients with audible third HS (S3).Accelerometer circuitry enhanced for HS was incorporated into non-functional ICDs. A study was conducted on 30 HF patients and 10 normal subjects without history of cardiac disease. The devices were taped to the skin surface over both left and right pectoral regions to simulate subcutaneous implants. A lightweight reference accelerometer was taped over the cardiac apex. Waveforms were recorded simultaneously with a surface electrocardiogram for 2 minutes. Algorithms were developed to perform off-line automatic detection of HS and HS time intervals (HSTIs).S1, S2, and S3 vibrations were detected in all accelerometer locations for all 40 subjects, including 16 subjects without an audible S3. A substantial proportion of S3 energy was infrasonic (<20 Hz). Extending the signal bandwidth accordingly increased HS amplitudes and the ability of S3 to separate HF patients from the normal subgroup. HSTIs also separated the subgroups and were less susceptible to patient-dependent acoustic propagation properties than amplitude measures.HS, including S3 amplitude and HSTIs, may be measured using PG-embedded circuitry at implant sites without special purpose leads. Further study is warranted to determine if relative changes in heart sounds measurements can be effective in applications such as remote ambulatory monitoring of HF progression and the detection of the onset of HF decompensation.

The heart is the principal organ that circulates blood. In normal conditions it produces four sounds for each cardiac cycle. However, most often only two sounds appear essential: S1 and S2. Two other sounds: S3 and S4, with lower amplitude than S1 or S2, appear occasionally in the cardiac cycle by the effect of disease or age. The presence of abnormal sounds in one cardiac cycle provide valuable information on various diseases. The aortic stenosis (AS), as being a valvular pathology, is characterized by a systolic murmur due to a narrowing of the aortic valve. The mitral stenosis (MS) is characterized by a diastolic murmur due to a reduction in the mitral valve. Early screening of these diseases is necessary; it's done by a simple technique known as: phonocardiography. Analysis of phonocardiograms signals using signal processing techniques can provide for clinicians useful information considered as a platform for significant decisions in their medical diagnosis. In this work two types of diseases were studied: aortic stenosis (AS) and mitral stenosis (MS). Each one presents six different cases. The application of the discrete wavelet transform (DWT) to analyse pathological severity of the (AS and MS was presented. Then, the calculation of various parameters was performed for each patient. This study examines the possibility of using the DWT in the analysis of pathological severity of AS and MS.

This paper presents the design and testing of a wireless sensor system developed using a Microchip PICDEM developer kit to acquire and monitor human heart sounds for phonocardiography applications. This system can serve as a cost-effective option to the recent developments in wireless phonocardiography sensors that have primarily focused on Bluetooth technology. This wireless sensor system has been designed and developed in-house using off-the-shelf components and open source software for remote and mobile applications. The small form factor (3.75 cm × 5 cm × 1 cm), high throughput (6,000 Hz data streaming rate), and low cost ($13 per unit for a 1,000 unit batch) of this wireless sensor system make it particularly attractive for phonocardiography and other sensing applications. The experimental results of sensor signal analysis using several signal characterization techniques suggest that this wireless sensor system can capture both fundamental heart sounds (S1 and S2), and is also capable of capturing abnormal heart sounds (S3 and S4) and heart murmurs without aliasing. The results of a denoising application using Wavelet Transform show that the undesirable noises of sensor signals in the surrounding environment can be reduced dramatically. The exercising experiment results also show that this proposed wireless PCG system can capture heart sounds over different heart conditions simulated by varying heart rates of six subjects over a range of 60-180 Hz through exercise testing.

BACKGROUND:

The prevalence of heart failure (HF) is increasing as the population ages, but its rapid diagnosis and phenotype identification remain challenging. We sought to determine whether acoustic cardiography can accurately identify HF and its phenotypes.

METHODS:

Three cohorts of patients were studied [94 with hypertension, 109 with HF and normal ejection fraction (HFNEF, EF≥50%) and 89 with HF and reduced ejection fraction (HFREF, EF<50%)]. All participants received acoustic cardiography and echocardiography examinations. Acoustic cardiographic parameters included S3 score (probability that the third heart sound exists), electromechanical activation time (EMAT, interval from Q wave to the first heart sound; EMAT/RR is EMAT normalized by heart rate), and systolic dysfunction index (SDI, a combination of EMAT/RR, S3 score, QRS duration and QR interval). Receiver operative characteristic curves were used to determine diagnostic utility of acoustic cardiography.

RESULTS:

EMAT/RR significantly differentiated HFNEF from hypertension (area under curve [AUC], 0.83; 95% confidence interval [CI], 0.77-0.89) with an EMAT/RR>11.54% yielded 55% sensitivity and 90% specificity. Similarly, an echo-measured E/e'>15 yielded 55% sensitivity, 90% specificity and 0.84 AUC in detecting HFNEF. Whereas SDI out-performed the other acoustic cardiographic parameters in differentiating HFREF from HFNEF (AUC, 0.81; 95% CI, 0.75-0.87), and an SDI>5.43 yielded 53% sensitivity and 91% specificity. The E/e' ratio had a similar diagnostic performance.

CONCLUSIONS:

Our study demonstrates that this bedside technology may be helpful in identifying HF and its phenotypes, especially when echocardiography is not immediately available.

The third and fourth heart sound (S3 and S4) are two abnormal heart sound components which are proved to be indicators of heart failure during diastolic period. The combination of using diastolic heart sounds with the standard ECG as a measurement of ventricular dysfunction may improve the noninvasive diagnosis and early detection of myocardial ischemia.In this paper, an adaptive method based on time-frequency analysis is proposed to detect the presence of S3 and S4. Heart sound signals during diastolic periods were analyzed with Hilbert-Huang Transform (HHT). A discrete plot of maximal instantaneous frequency and its amplitude was generated and clustered. S3 and S4 were recognized by the clustered points, and performance of the method was further enhanced by period definition and iteration tracking.Using the proposed method, S3 and S4 could be detected adaptively in a same method. 90.3% of heart sound cycles with S3 were detected using our method, 9.6% were missed, and 9.6% were false positive. 94% of S4 were detected using our method, 5.5% were missed, and 16% were false positive.The proposed method is adaptive for detecting low-amplitude and low-frequency S3 and S4 simultaneously compared with previous detection methods, which would be practical in primary care.

The purpose of our study was to investigate the frequency of the third heart sound (S3) of athletes after exercise, and to determine whether the frequency and amplitude of S3 were related to cardiac function. The phonocardiogram exercise test (PCGET) was used in this study, and healthy volunteers consisting of 84 athletes (age 21.0±1.7 years; 62 males and 22 females) and 45 non-athletes (age 24.1±2.0 years; 33 males and 12 females) were enrolled. All subjects were healthy except one with a cardiac murmur without known cause. Immediately after exercise, S3 occurred in 21 athletes (25.0%) and 10 non-athletes (22.2%) during PCGET. There were very significant differences between pre-exercise and post-exercise in the frequency of S3 (P<0.01), and no significant difference between athletes and nonathletes (P>0.05). The prevalence of S3/S2≥1 was significantly (P<0.05) higher for the athlete group (47.1%) as compared to the non-athlete group (10%). Those results indicated that the emergence of S3 was an indicator of heart burden, and S3 after exercise in the athlete group was physiological. Our study showed that the amplitude of S3 had a very sensitive response to cardiac function reduction and S3/S2≥1 could eventually be used to assess cardiac fatigue states.

Adequately recording diastolic heart sounds and systolic time intervals over longer periods is difficult. Thus, information on the circadian variation of these parameters in an ambulatory population is lacking. Moreover, age-related changes in the prevalence of diastolic heart sounds and measurements of systolic time intervals in an asymptomatic population have not been studied in continuous recordings.Diastolic heart sounds and systolic time intervals will have age and circadian variations that reflect known changes in cardiac function due to aging and circadian rhythms.We studied 128 asymptomatic subjects wearing an ambulatory monitor with acoustic cardiography. The recording spanned a mean duration of 14 hours, including sleep. Data were analyzed for the presence of third (S3) and fourth (S4) heart sounds and for systolic time intervals.In these asymptomatic subjects, S3 was significantly more prevalent in those age <40 years than in those age >40 years, and significantly more pronounced during sleep in the younger group. Also, S4 was significantly more prevalent in those age >40 years and significantly more pronounced during sleep in those age >40 years. In contrast, time intervals reflecting systolic function showed less circadian variation and less worsening with age.The nocturnal increase of S4 in the elderly reflects diastolic impairment-likely a result of changes in diastolic filling patterns with increasing age. An S3 after the age of 40 is a relatively uncommon finding and therefore should be a specific sign of cardiac disease. Continuous monitoring of diastolic heart sounds and systolic time intervals is possible using acoustic cardiography.