A new phonocardiographic recording system

Lukkarinen, S.; Noponen, A.-L.; Sikio, K.; Angerla, A.

doi:10.1109/cic.1997.647844

Cited by 21 publications

(11 citation statements)

References 8 publications

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“…By recording the sound information on to a laptop computer both sound replay and graphical display can add to the understanding of various heart sound conditions. The value of such an approach [1] and the graphical display of results [2] have been seen as good aids to diagnosis. Data has been collected over a number of years from patients at the Brighton and Sussex University Hospital (85 patients in 1995/6, 25 in 2000/01 and a recent batch of 15 in 2003) using a lab-built digital stethoscope and standard interface to a laptop.…”

Section: Input Datamentioning

confidence: 99%

Heart sound analysis comparing wavelet and autoregressive techniques

Torry

2003

Computers in Cardiology, 2003

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Three different types of wavelets (Morlet, Bessel filter and Chirp) and four different types of autoregressive modelling techniques (Burg, Forward, Backward and Modified Covariance) Input dataThe aim is to seek a better visualisation of heart sound using modern digital signal processing techniques. By recording the sound information on to a laptop computer both sound replay and graphical display can add to the understanding of various heart sound conditions. The value of such an approach [1] and the graphical display of results [2] have been seen as good aids to diagnosis. Data has been collected over a number of years from patients at the Brighton and Sussex University Hospital (85 patients in 1995/6, 25 in 2000/01 and a recent batch of 15 in 2003) using a lab-built digital stethoscope and standard interface to a laptop. Analogue measurement used the HP21050A sensor for the early recordings and a good electret microphone for the recent set. The bandwidth is limited to 10Hz to 700Hz using third and fourth order Chebyshev filters respectively. The signal is digitised with 12-bit resolution at a relatively high sampling rate of 4096Hz.Simultaneous recording were taken of the patients' ECG. A bulky 486 PC and large interface box was used for the 1995/6 and 2000/1 recordings but has now been updated to a manageable laptop, making the 5 mile trip to the local hospital easier. All patients were seen by a resident hospital cardiologist prior to undergoing probing from university postgraduates. In most cases there is supporting written documentation from the cardiologist and in some cases ultrasound scan information is available.Once recorded, the off-line process of analysis can begin. Matlab™ provides, at ones fingertips, a range of signal processing algorithms which can be adapted for this heart sound application. Analysis begins by segmenting the input waveform into individual heart sound cycles. The R wave from the ECG signal is used as the cycle divider and as an indication of the approximate start of S1. Wavelet and AR techniques are best applied on a cycle-bycycle basis. Further analysis requires the subdivision into the S1, systolic, S2 and diastolic phases. After which an energy calculation can be made to assess the relative energy in each phase and in particular give a value to the level of severity of any systolic murmur. Wavelet analysis techniquesThe Morlet wavelet has been used in recent years by a number of researchers [3]. This is not an orthogonal wavelet as required by those who do compression and decompression, but is merely a means of looking at some of the time-frequency information of a signal. Selecting a wavelet then becomes a process of seeing which wavelet suits the style of the signal to be investigated rather than being constrained by the compression process.The 'wavelet manager' in Matlab™ is somewhat cumbersome but for a specific wavelet analysis it can be reduced to a few lines of code:

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Section: Input Datamentioning

confidence: 99%

Heart sound analysis comparing wavelet and autoregressive techniques

Torry

2003

Computers in Cardiology, 2003

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“…discriminate among heart diseases resulting in similar heart sound signals), especially in rural areas, in homecare and in primary healthcare. These systems are usually based on digital electronic stethoscopes (for heart sound signals acquisition) and computers (for the analysis of the acquired signals based on appropriate algorithms) (Myint and Dillard, 2001;Lukkarinen et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

A multiple decision trees architecture for medical diagnosis: The differentiation of opening snap, second heart sound split and third heart sound

Stasis

Loukis

Pavlopoulos

et al. 2004

Computational Management Science

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In this paper a Decision Support System Architecture is proposed for the heart sound diagnosis problem, and in general for complex medical diagnosis problems. It is based on the division of a complex diagnostic problem into simpler sub-problems; each of them is handled by a specialized decision tree. This Multiple Decision Trees Architecture in general consists of a network of detection decision trees and arbitration decision trees, and can also incorporate other classification methods as well (e.g. patterns recognition, neural networks, etc.). The initial motivation for developing this Multiple Decision Trees Architecture has been the problem of differentiation among Opening Snap (OS), 2nd Heart Sound Split (A2_P2), and 3rd Heart Sound (S3), which is a crucial and at the same time difficult and complicated part of the heart sound diagnosis problem. The Multiple Decision Tree Architecture developed for the above diagnosis/differentiation problem has been tested with real heart sound signals, and its performance and generalisation capabilities were found to be higher than the previous ‘traditional’ architectures. Copyright Springer-Verlag Berlin/Heidelberg 2004Heart sound diagnosis, Medical diagnosis, Decision trees, Decision support systems, Opening snap, 3rd heart sound, 2nd heart sound split,

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“…The analysis filterbank suitable for the subband codec has the form described by (1), where h m (n) is the subband analysis filter, m is the subband index, M=16 is the number of subbands and h p (n) is the prototype low-pass filter. The filter length, L was set to L a = L s .…”

Section: Low Resource Subband Codecmentioning

confidence: 99%

“…The resulting subband signals do not contain additional aliased images because the original over-sampled subband signal is band-limited to π/4. Secondly, to obtain critically-sampled, real-valued data, the method described in [4] is used to generate the cosine-modulated filterbank in (1).…”

Section: Low Resource Subband Codecmentioning

confidence: 99%

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An Ultra-Low Power Subband-Based Electronic Stethoscope

Johnson¹,

Hermann²,

Witter³

et al.

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings

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Electronic stethoscopes are able to offer signal amplification and other benefits over traditional stethoscopes. However, many electronic stethoscopes rely on a personal computer for their signal processing, which reduces portability and requires a relatively large amount of power. This paper presents a low power, portable electronic stethoscope system that is based on a signal processing approach using an over-sampled filterbank. This system is implemented on an ultra-low resource DSP system. The stethoscope incorporates multiple filtering modes as well as audio record and playback (full and half speed) functionality.

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A new phonocardiographic recording system

Cited by 21 publications

References 8 publications

Heart sound analysis comparing wavelet and autoregressive techniques

Heart sound analysis comparing wavelet and autoregressive techniques

A multiple decision trees architecture for medical diagnosis: The differentiation of opening snap, second heart sound split and third heart sound

An Ultra-Low Power Subband-Based Electronic Stethoscope

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