Computerised analysis of auscultatory sounds associated with vascular patency of haemodialysis access

Vascular access for renal dialysis is a lifeline for about 120 000 individuals in the United States. Stethoscope auscultation of vascular sounds has some utility in the assessment of access patency, yet can be highly skill-dependent. The objective of the study was to identify acoustic parameters that are related to changes in vascular access patency. The underlying hypothesis is that stenoses of haemodialysis access vessels or grafts cause vascular sound changes that are detectable using computerised data acquisition and analysis. Eleven patients participated in the study. Their vascular sounds were recorded before and after angiography, which was accompanied by angioplasty in most patients. The sounds were acquired using two electronic stethoscopes and then digitised and analysed on a personal computer. Vessel stenosis changes were found to be associated with changes in acoustic amplitude and/or spectral energy distribution. Certain acoustic parameters correlated well (correlation coefficient=0.98, p<0.0001) with the change in the degree of stenosis, suggesting that stenosis severity may be predictable from these parameters. Parameters also appeared to be sensitive to modest diameter changes (>20%), (p<0.005, Wilcoxon rank sum test). These results suggest that computerised analysis of vascular sounds may be useful in vessel patency surveillance. Further testing using longitudinal studies may be warranted.     

健康人群的肺部听诊情况调查及结果分析

目的：获得正常健康人群肺部听诊结果分布情况。方法用专用肺部听诊设备对1896名健康成人行肺部听诊并录音,由呼吸专业医师根据录音判断肺部听诊情况。肺部听诊采集录取肺部喉部、左上、右上、左中、右中、左下、右下、左后、右后9个部位肺音。统计分析各部位啰音分布百分比。结果符合入选条件者1450人,9个听诊部位全部正常者1014人(69.9%),至少有1个部位听诊有啰音者436人(30.06%),至少有2个部位有啰音者190人(13.10%),至少有3个部位有啰音者81人(占5.58%)；出现干啰音比例由高至低依次为：左上(13.4%)、左中和左后(1.4%)、喉部和右上和左下(1.2%)、右下(1.0%)、右后(0.9%)；湿啰音比例由高至低依次为：右下(6.6%)、左后(5.9%)、右中(5.8%)、左下(5.1%)、右后(2.8%)、喉部(2.1%)、右上(1.9%)、左上(0.8%)；左中、右下和左后同时具有干啰音和湿啰音各2例(0.1%)。各听诊部位间听诊啰音分布及听诊部位间啰音分布情况差异无统计学意义(P >0.05)。结论健康成人肺部啰音并非少见,其临床意义有待进一步研究。     

Paediatricians�� validation of learning objectives in paediatric cardiology

BACKGROUND

The current objectives for teaching paediatric cardiology to paediatric residents have not been validated and may not be relevant to current paediatric practice.

OBJECTIVES

To validate the cardiology component of the Royal College of Physician and Surgeons of Canada’s objectives for training paediatricians.

METHODS

A questionnaire was sent to practising paediatricians in Atlantic Canada. The questions were based on the Royal College of Physician and Surgeons of Canada’s training objectives. The frequency of problems seen, confidence in assessment and management of problems, and reasons for referral were identified. Clinical vignettes were followed by short questions. The outpatient referrals were reviewed to validate the questionnaire responses.

RESULTS

One hundred fifty-one questionnaires were mailed and the response rate was 60%. Murmurs were the most common problem encountered (92%). Syncope (9%), Kawasaki disease (8%) and chest pain (6%) were less frequently encountered. Paediatricians were confident in assessing and managing problems despite the low frequency of encounters. Less confidence was expressed regarding physical examination skills and interpretation of electrocardiograms. Uncertainty of the diagnosis was the most common reason for patient referral, with parental anxiety and medicolegal concerns accounting for 24% and 7% of referrals, respectively. Syncope with exercise was relatively poorly recognized as a worrisome symptom.

CONCLUSIONS

Most cardiology objectives for general paediatric training remain relevant and appropriate to clinical practice. Physical examination skills, electrocardiogram interpretation and the assessment of syncope need to be emphasized.     

Neural classification of lung sounds using wavelet coefficients

Electronic auscultation is an efficient technique to evaluate the condition of respiratory system using lung sounds. As lung sound signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper deals with a novel method of analysis of lung sound signals using wavelet transform, and classification using artificial neural network (ANN). Lung sound signals were decomposed into the frequency subbands using wavelet transform and a set of statistical features was extracted from the subbands to represent the distribution of wavelet coefficients. An ANN based system, trained using the resilient back propagation algorithm, was implemented to classify the lung sounds to one of the six categories: normal, wheeze, crackle, squawk, stridor, or rhonchus.     

Automated cardiac auscultation for detection of pathologic heart murmurs

Experienced cardiologists can usually recognize pathologic heart murmurs with high sensitivity and specificity, although nonspecialists with less clinical experience may have more difficulty. Harsh, pansystolic murmurs of intensity grade ≥3 at the left upper sternal border (LUSB) are likely to be associated with pathology. In this study, we designed a system for automatically detecting systolic murmurs due to a variety of conditions and examined the correlation between relative murmur intensity and likelihood of pathology. Cardiac auscultatory examinations of 194 children and young adults were recorded, digitized, and stored along with corresponding echocardiographic diagnoses, and automated spectral analysis using continuous wavelet transforms was performed. Patients without heart disease and either no murmur or an innocent murmur (n= 95) were compared to patients with a variety of cardiac diagnoses and a pathologic systolic murmur present at the LUSB (n= 99). The sensitivity and specificity of the automated system for detecting pathologic murmurs with intensity grade ≥2 were both 96%, and for grade ≥3 murmurs they were 100%. Automated cardiac auscultation and interpretation may be useful as a diagnostic aid to support clinical decision making.     

Diagnosing Aortic Valve Stenosis by Parameter Extraction of Heart Sound Signals

The objective of this study was to develop an automatic signal analysis system for heart sound diagnosis. This should support the general practitioner in discovering aortic valve stenoses at an early stage to avoid or decrease the number of surgical interventions. The applied analysis method is based on classification of heart sound signals utilising parameter extraction. From the wavelet decomposition of a representative heart cycle as well as from the Short Time Fourier Transform (STFT) and the Wavelet Transform (WT) spectra new time series were derived. In several segments, parameters were extracted and analysed. In addition, features of the Fast Fourier Transform (FFT) of the raw signal were examined. In this study, 206 patients were enrolled, 159 with no heart valve disease or any other heart valve disease but aortic valve stenosis and 47 suffering from aortic valve stenosis in a mild, moderate or severe stage. To separate the groups, a linear discriminant function analysis was applied leading to a reduced parameter set. The introduced two classification stage (CS) system for automatic detection of aortic valve stenoses achieves a high sensitivity of 100% for moderate and severe aortic valve stenosis and a sensitivity of 75% for mild aortic valve stenosis. A specificity of 93.7% for patients without aortic valve stenosis is provided. The developed method is robust, cost effective and easy to use, and could, therefore, be a suitable method to diagnose aortic valve stenosis by general practitioners.     

Detection of the third heart sound using a tailored wavelet approach: Method verification

Heart sounds can be considered as mechanical fingerprints of myocardial function. The third heart sound normally occurs in children but disappears with maturation. The sound can also appear in patients with heart failure. The sound is characterised by its low-amplitude and low-frequency content, which makes it difficult to identify by the traditional use of the stethoscope. A wavelet-based method has recently been developed for detection of the third heart sound. This study investigated if the third heart sound could be identified in patients with heart failure using this detection method. The method was also compared with auscultation using conventional phonocardiography and with characterisation of the patients with echocardiography. In the first study, 87% of the third heart sounds were detected using the wavelet method, 12% were missed, and 6% were false positive. In study 2, the waveletdetection method identified 87% of the patients using the third heart sound, and regular phonocardiography identified two (25%) of the subjects.     

The need for standards in recording and analysing respiratory sounds

Respiratory sounds (RSs) recorded from the chest and trachea are nowadays being electronically analysed by many investigators with a view to (i) determining the mechanisms of their production, and (ii) to develop automated diagnostic systems based on RS analysis, that objectively categorise RS as being associated with health or respiratory diseases. However, one problem that hampers this type of research is that almost every RS investigation team uses different equipment, protocols and analysis methods which, to varying degrees, makes inter-investigator results difficult to compare. The review first discusses the many variables involved in RS recording and analysis, and the different approaches used by different investigators, to highlight this problem and its consequences. Secondly, although the review cannot propose immediately acceptable guidelines and standards for RS analysis, it proposes a ‘seed’ set of guidelines that are ‘up for discussion’ between investigators in the field, the final goal being to inject a degree of standardisation in equipment and methods that are acceptable to all involved.     

对胎心电子监护和间歇性胎心听诊的重新审视

20世纪50年代之前,间歇性胎心听诊(intermittent aus-cultation,IA)是产时胎心监护的主要方法。自50年代末至60年代初,胎儿电子监护(electronic fetal monitoring,EFM)进入了研究阶段。最初,这一技术被应用于高危孕妇的产程中,20世纪70年代,在通过随机对照试验证实它的效用之前,EFM在临床上的运用即迅速扩张;到80年代,EFM在欧美等发达