(Circulation. 2001;103:2711.)
© 2001 American Heart Association, Inc.
Clinical Investigation and Reports |
Artificial Neural Network–Based Method of Screening Heart Murmurs in Children
From the University of Colorado Health Sciences Center, the Children’s Hospital, Denver (C.G.D., R.S., L.V-C.), and the Department of Mechanical Engineering, University of Colorado, Boulder (S.B., J.H., R.S., R.L.M.), Colo.
Correspondence to Curt G. DeGroff, MD, University of Colorado Health Science Center, the Children’s Hospital, 1056 E 19th Ave, B-100 Denver, CO 80218. E-mail degroff.curt{at}tchden.org
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Abstract |
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 Top Abstract IntroductionMethodsResultsDiscussionConclusionsAppendix AAppendix BAppendix CReferences |
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Background—Early recognition of heart disease is an important goal in pediatrics. Efforts in developing an inexpensive screening device that can assist in the differentiation between innocent and pathological heart murmurs have met with limited success. Artificial neural networks (ANNs) are valuable tools used in complex pattern recognition and classification tasks. The aim of the present study was to train an ANN to distinguish between innocent and pathological murmurs effectively.
Methods and Results—Using an electronic stethoscope, heart sounds were recorded from 69 patients (37 pathological and 32 innocent murmurs). Sound samples were processed using digital signal analysis and fed into a custom ANN. With optimal settings, sensitivities and specificities of 100% were obtained on the data collected with the ANN classification system developed. For future unknowns, our results suggest the generalization would improve with better representation of all classes in the training data.
Conclusion—We demonstrated that ANNs show significant potential in their use as an accurate diagnostic tool for the classification of heart sound data into innocent and pathological classes. This technology offers great promise for the development of a device for high-volume screening of children for heart disease.
Key Words: heart murmurs • neural networks (computer) • child • heart defects, congenital
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Introduction |
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TopAbstractIntroductionMethodsResultsDiscussionConclusionsAppendix AAppendix BAppendix CReferences |
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The incidence of heart murmurs in the pediatric population is reportedly as high as 77% to 95%. However, <1% of this population has heart disease.1 Early recognition is an important goal,2 and equally important is avoiding misdiagnosing a pathological heart murmur in a healthy child without heart disease.2 To acquire high-quality auscultation skills requires the guidance of an experienced instructor using a sizable number of patients along with frequent practice.1 2 Unfortunately, the interpretation of auscultation findings overall remains prone to error.3 4 5 6 Imaging technologies can provide more direct evidence of heart disease; however, they are generally more costly. Efforts to develop an inexpensive screening device that can assist in the differentiation between innocent and pathological heart murmurs have met with limited success.7
There has been much excitement in the scientific literature
in recent years regarding artificial neural networks
(ANNs)8 9 in
medicine10 and,
specifically, in cardiology
applications.7 8 11 12 13 14 15 16 17 18 19 20
ANNs are valuable tools used in complex pattern recognition and
classification tasks. They learn complex interactions among inputs and
identify relations in input data that may not be apparent to human
analysis.14 The most
common type of ANN consists of 3 layers of processing units: the input
layer, the hidden layer, and the output layer connected in sequence
(Figure 1
), details of which can be found in Appendix
.
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Studies of ANNs in cardiology have been mainly concerned with the evaluation of ECG signals.14 15 16 18 19 20 There have been several studies on the use of ANNs on heart sounds with limited results and applicability.7 11 12 21 The aim of the present study was to train an ANN to distinguish between innocent and pathological murmurs effectively.
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Methods |
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TopAbstractIntroductionMethodsResultsDiscussionConclusionsAppendix AAppendix BAppendix CReferences |
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Study Subjects
Digital heart sound recordings were obtained from 69 pediatric patients who were referred to our cardiology clinic for evaluation (aged 1 week to 15 years; mean age, 2 years). Under Internal Review Board protocol, all participants and/or parents gave written, informed consent to participate in this study. The diagnosis was confirmed by 1 of 5 pediatric cardiologists. Echocardiographic confirmation was performed on 100% of the patients with pathological heart murmurs and 70% of patients with innocent heart murmurs.
Recordings
Using an electronic stethoscope (Cambridge Heart
Sound Microphone; frequency range, 40 to 600 Hz) and a personal
computer (sampling at 44 kHz), heart sounds were recorded at the
left midsternal border in the supine position in a standard examination
room for all patients. Younger patients were placed in a darkened
examination room with minimal stimuli to encourage cooperation. No
sedation was used. For each patient, 2 separate heart sound
recordings were acquired. One recording location was
chosen with the expectation that murmurs generally not heard from this
location (using standard stethoscopic methods) would be detected by the
electronic microphone used due to its sensitivity.
Two heart sound recordings for each patient of 
8
heart cycles in length were recorded. Subsequently, one sound
sample of 3 repre-sentative consecutive heart
cycles was chosen from these 2 recordings by one of the
investigators (C.G.D.) for all 69 patients. The investigator chose the
optimal sound sample by listening to the entirety of the 2
recordings and qualitatively determining the sound sample (of 3
consecutive heart cycles) with the least amount of extraneous noise,
such as breathing, talking, motion artifact, and room noises. This
optimal sound sample was then processed using digital signal
analysis
(Figure 2).
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Signal Analysis
A normalized energy spectrum of the sound data was
obtained by applying a Fast Fourier
Transform22 to the heart
sounds. Various spectral resolutions (1, 3, and 5 Hz) and frequency
ranges (0 to 90, 0 to 150, 0 to 210, 0 to 255, and 0 to 300 Hz) were
used as input into the ANN to optimize these parameters to
obtain the most favorable results.
Artificial Neural Network
We used customized ANN software developed in our
laboratory (CUANN; see Appendix B). ANNs were trained to discriminate
between normal and pathological examples. Because of the size of the
available data, the Jack-Knifing
method23 was used. The
Jack-Knifing method is an iterative process in which one observation is
recruited each time for validation. A classifier is trained using the
remaining data and validated on the single, left-out validation point.
This ensures that the validation is unbiased, because the classifier
does not see the validation point during its training. One by one, each
available example is recruited for validation. The approach measures
the power of the classification approach rather than of one specific
classifier.
Each of the 69 data sets was recruited for validation, one
at a time, creating 69 separately trained ANNs. For each spectral
resolution (1, 3, and 5 Hz) and frequency range considered (0 to 90, 0
to 150, and 0 to 210 Hz), a set of 69 neural networks with identical
numbers of input, hidden, and output neurons was created
(Table 1).
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Statistical Analysis
Standard equations for sensitivity and specificity
were
used.24
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Results |
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TopAbstractIntroductionMethodsResultsDiscussionConclusionsAppendix AAppendix BAppendix CReferences |
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Study Subjects
Pathologies included aortic and pulmonary stenosis/insufficiency, atrial and ventricular septal defects, patent ductus arteriosus, and mitral regurgitation (Figure 3
). By spectral Doppler
echocardiography, all patients with aortic
stenosis had moderate stenosis (gradient, 30 to 50
mm Hg), and all patients with pulmonary stenosis had
mild stenosis (gradient, <20 mm Hg).
Peripheral pulmonary stenosis murmurs were
considered pathological because of the potential necessity of follow-up
required for some of these patients. Innocent heart murmurs consisted
of Still’s murmurs and pulmonary flow murmurs. No venous hum
murmurs were included in this study.
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Recordings
For the majority of patients, the entire
recording procedure took <5 minutes (maximum, 10 minutes). All
of the patient recordings were deemed of acceptable quality by
one investigator (C.G.D.).
ANN Predictions
Table 2 shows the results obtained with a spectral
resolution of 3 Hz and various frequency ranges. Sensitivities and
specificities >90% were obtained.
Figure 4A shows the predictions of the 69 ANNs (with a
spectral range of 0 to 150 Hz and a spectral resolution of 3 Hz)
constituting a feature set of 50 constituent elements. ANN prediction
is plotted on the y axis versus
the corresponding validation example number on the
x axis. The data were arranged
in a specific, constant order. The first 37 examples (cases 1 through
37) were pathological murmurs, and the next 32 (cases 38 through 69)
were innocent murmurs. The horizontal dashed line represents
the decision threshold. Setting the threshold to either extremity (0 or
1) results in trivial results so that all examples are classified as
normal or pathological. With a threshold of 0.8, a sensitivity of 100%
and a specificity of 92% were obtained. Observe the erroneous
classifications with respect to this threshold marked by bull’s-eyes.
Interestingly, increasing the spectral range past 0 to 150 Hz while
keeping the spectral resolution to 3 Hz produced worsening results
(Table 2), with the ANN predictions not as tightly
distributed
(Figure 4B).
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To further improve the performance, the spectral
resolution was increased to 1 Hz. In the frequency range of 0 to 90 Hz,
the performance was inadequate. However, it improved as the
spectrum was expanded
(Table 2
). With the upper limit of the frequency range
increased from 90 to 150 Hz, the accuracy of the classifier system
improved to 100% sensitivity and specificity. Increasing the upper
limit from 150 to 210 Hz continued to improve the classifier system
(Table 2 and
Figure 4C). However, when the upper limit was increased
still further to 255 Hz, the trend was reversed. Although 100%
sensitivity and specificity were maintained, the ANN predictions were
not as tightly distributed (data not shown), in a manner similar to
what occurred with the spectral resolution of 3
Hz.
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Discussion |
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TopAbstractIntroductionMethodsResultsDiscussionConclusionsAppendix AAppendix BAppendix CReferences |
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Cardiac auscultation continues to be the healthcare professional’s primary tool for distinguishing between innocent and pathological heart murmurs in pediatric patients. The sound characteristics for heart lesions have been previously described.25 For medical persons to acquire high-quality auscultation skills requires the guidance of an experienced instructor using a sizable number of patients along with frequent practice.1 2 Unfortunately, the interpretation of auscultation findings is prone to error.3 4 5 6 In addition, the human ear cannot appreciate many of the subtleties of heart sounds.26 Therefore, a device that can assist in the differentiation between innocent and pathological heart murmurs would be quite useful. Efforts to date have met with limited success.7 11 12 21 27 28 29 30 31 32 One of these studies examined the sounds from prosthetic heart valves.11 Several of these studies are simply observations of the time frequency analysis of innocent and pathological heart murmurs, with no emphasis on developing automated diagnostic algorithms.29 30 All of these previous studies (including those using ANNs) have generally focused on determining if a heart murmur exists, with a lack of emphasis on developing diagnostic algorithms differentiating between innocent versus pathological murmurs.7 12 21 27 28 31 32
In this study, 100% sensitivity and specificity were obtained for the ANN distinguishing between the 69 innocent and pathological heart murmurs presented to it using a spectral resolution of 1 Hz and a spectrum of 0 to 210 Hz. In general, a finer resolution and larger spectrum yielded improved results. The trend in improvement of classifier accuracy with wider energy spectrum may be explained as follows. As the energy spectrum is expanded, the ANN classifier has more information to work with, and its accuracy tends to increase. However, the increased computational load on the ANN may offset this benefit, and it is seen that at a certain point, the trend is reversed. Also, it is common knowledge that a pattern recognition that can handle a larger feature set is better unless the larger feature set contains redundant information. The trend in improved classification performance with higher Fast Fourier Transform resolution was confirmed by decreasing the resolution to 5 Hz (data not shown). Accuracy comparable to 1 Hz or 3 Hz levels was never achieved at this resolution.
Although we deployed all 69 examples for validation, only one example served for validation each time, thus creating 69 separately trained ANNs. The question arose as to whether a classifier system comprising a set of ANNs accruing from the Jack-Knifing approach could be field-deployed. Under such a scheme, the individual ANNs would vote in the classification of a previously unseen observation. To investigate this matter and having no additional data, the data were divided into 2 sets. (1) A set of 54 examples was earmarked as training data for the development of a system of 54 ANNs following the Jack-Knifing scheme, and (2) a set of 15 examples was reserved as field-testing data exclusively to validate the 54 ANNs as multiple experts, with the decision effected by a rule of simple majority. Using the validation set, the system of ANNs was able to classify 7 of 9 pathological examples and 5 of 6 innocent examples correctly. Both of the misclassified pathological examples, a case with pulmonary stenosis and a case with an atrial septal defect, belonged to pathological classes that were grossly under-represented in the training data. When these misclassified pathological examples and the misclassified normal example were reintroduced into the training data set, a threshold could be established with respect to which the resulting classifier system of 57 ANNs was fully accurate when field-tested on the 12 validation examples. These results suggest that ANN generalization would improve with better representation of all classes in the training data, for which more data would have to be collected.
Finally, the current results include training and testing of the classifier system with only one class of aortic stenosis (moderate) and only one class of pulmonary stenosis (mild). Clearly, a wide spectrum of murmurs for each type of pathological case has not yet been presented to the network. However, the classifier system did perform well with a wide range of sound "signatures" found to be present in the patients with murmurs from ventricular septal defects (Appendix C).
Overall, we propose that as additional data are collected, a point will be reached when the important pathological types will each be adequately represented and the generalization capability of the ANN classifier system developed by Jack-Knifing will stabilize at some high accuracy. At that point, misclassifications will be rare and field deployment will be possible. During the stabilizing process, a particular frequency range, spectral resolution, and threshold will be selected to optimize results. Also, after this point, whether the classifier system will be designed so that it continues to "learn" is beyond the scope of this discussion.
Limitations and Future Work
The process of selecting the optimal 3 consecutive
heart cycles required an intelligent selection process. In developing a
screening device, automating this selection
process33 will need to be
investigated. The performance of the ANN with the addition of
noise processes (eg, from an uncooperative patient) to the input signal
will need to be investigated. All of our 69 patients were cooperative
enough for us to obtain adequate heart sound recordings. An
intelligent automated selection algorithm will also need to determine
if an adequate sample exists at all.
The most pressing need at present is to collect more data., In future efforts, it would be impractical to consider that the classifier system could be introduced to all possible heart murmur "signatures." Therefore, future efforts will focus on presenting the ANN with a wide spectrum of each of the common pathological cases studied here, both for training and testing purposes. However, our results here are quite promising with the limited data set available.
If, after additional training data are presented to the ANN classifier system, the system is unable to maintain the accuracy that has been shown here, recordings from the standard 5 cardiac auscultation sites will be considered as inputs. This may be necessary for murmurs not generally heard well at the one recording location chosen (left midsternal border). However, the sensitivity of the electronic microphone used will potentially grant us the economy of one recording location.
Finally, there will most likely be classes of patients with heart lesions for which a screening device using these algorithms may be expected to produce false negatives. Therefore, in patients for whom such a screening device determines that a pathological murmur does not exist, it would simply declare: "no abnormal cardiac murmurs detected." Two examples are newborns with cardiac shunt lesions in whom murmurs typically appear only after pulmonary pressures begin to drop and those with heart lesions whose murmurs may appear only during examination with postural changes.
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Conclusions |
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TopAbstractIntroductionMethodsResultsDiscussionConclusionsAppendix AAppendix BAppendix CReferences |
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We demonstrated that ANNs show significant potential in their use as an accurate diagnostic tool for the classification of heart sound data into innocent and pathological classes. Of course the evaluation of a child with a heart murmur is far more inclusive that just the auscultation of that murmur. Therefore, such a device is not seen as replacing the need for a clinician’s assessment in a child found to have a heart murmur. Such a device may, however, assist a clinician in rendering an opinion concerning a murmur. In addition, this technology offers great promise for the development of a device for high-volume screening of children for heart disease. Eventually, this work is expected to lead to an automatic screening device with additional capabilities of predicting selected heart conditions.
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Appendix A |
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TopAbstractIntroductionMethodsResultsDiscussionConclusionsAppendix AAppendix BAppendix CReferences |
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An ANN is a highly interconnected system of computational nodes or neurons. In an ANN of the feed-forward type, the neurons are organized into layers. Typically, there is an input layer into which the input vector (independent variables) is fed in, an output layer that produces the output vector (dependent variables), and one or more layers in between. A typical ANN is shown in Figure 1
. It has an input layer, a hidden layer, and an
output layer. There are connections going from any one neuron in one
layer to all the neurons in the next layer. Each connection is
associated with a weight. Such an ANN can be trained to learn
input-output mapping by showing it examples of the mapping. During the
training procedure, the weights of the ANN are adjusted such that the
ANN converges to the output of the training data. Here, an input vector
is presented at the input layer and run through the ANN to
obtain the output vector. The output of any layer is propagated as the
input to the following layer. The following
equation relates the output of a neuron in the first layer to its input from the
final minus first layer. The neuronal output is also referred to as its
activation.
 | (1) |
 | (2) |
To train an ANN for developing input-output mapping, data are required that are representative of the mapping. The first step of training is the forward pass, which consists of calculating the output vector by running the input vector through the ANN. This is followed by a backward pass, where the error derivatives are calculated for each weight. The error derivatives for a weight are summed until all the data points have been run through the network once. This constitutes an epoch. The weights are updated after each epoch such that the ANN error decreases. For a classification task, the output neurons of the ANN represent individual classes where, typically, a binary classification scheme is used.
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Appendix B |
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TopAbstractIntroductionMethodsResultsDiscussionConclusionsAppendix AAppendix BAppendix CReferences |
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For this project, we used our in-house ANN software (CUANN),34 35 which is designed for efficient development and deployment of a feed-forward ANN. It implements a feed-forward ANN using the back-propagation learning algorithm sketched in the preceding section.
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Appendix C |
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TopAbstractIntroductionMethodsResultsDiscussionConclusionsAppendix AAppendix BAppendix CReferences |
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It was important to know whether the examples of a pathological class provided an adequate representation of that class. The evaluation was performed by analyzing the extent of dissimilarity within the examples of the same pathology in our data using the average correlation metric.21 In general, the average correlation between 2 classes is defined as follows:
 | (3) |
The same metric, applied to just one class, provides a
measure of intraclass similarity. In this case, the
metric is evaluated as follows:
 | (4) |
We compared the set of examples representing ventricular septal defect murmurs with the set of examples with innocent murmurs. The intraclass correlation metric was 78% for the defects and 77% for the innocent murmurs. Because it was known that the 32 instances of innocent murmurs in our data set included disparate examples, we concluded that the set of ventricular septal defect examples was also heterogeneous.
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Acknowledgments |
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We gratefully acknowledge The Children’s Hospital Research Institute and Micro-Electronic Devices in Cardiovascular Applications (MEDICA). Interdisciplinary Center Denver, Colorado, for partial financial support and for facilitating this project.
Received December 31, 2000; revision received March 19, 2001; accepted March 21, 2001.
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References |
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TopAbstractIntroductionMethodsResultsDiscussionConclusionsAppendix AAppendix BAppendix CReferences |
|---|
-
Garson
A, Bricker J, McNamara D. The Science and
Practice of Pediatric Cardiology.
Philadelphia, Pa: Lea & Febiger;
1990:2203–2212.
-
Pelech A. The
cardiac murmur. Pediatr Clin North
Am.. 1998;45:107–122.[Medline]
[Order article via Infotrieve]
-
Rangaraj M,
Murthy I, Quantitative analysis of the phonocardiogram for
detection of murmurs. J Biomed
Eng. 1979;1:247–252.[Medline]
[Order article via Infotrieve]
-
Lok C, Morgan
C, Ranganathan N. The accuracy and intraobserver agreement in detecting
the ‘gallop sounds’ by cardiac auscultation.
Chest. 1998;114:1283–1289.
[Abstract/Free Full Text]
-
VanOort A, Le
Blanc-Botden M, De-Boo T, et al. The vibratory innocent heart murmur in
school children: difference in auscultatory findings between school
medical officers and a pediatric cardiologist.
Pediatr Cardiol. 1994;15:282–287.[Medline]
[Order article via Infotrieve]
-
Pelech A.
Evaluation of the pediatric patient with a cardiac murmur.
Pediatr Clin North Am.. 1999;46:167–188.[Medline]
[Order article via Infotrieve]
-
Cathers I.
Neural network assisted cardiac auscultation.
Artif Intell Med. 1995;7:53–66.[Medline]
[Order article via Infotrieve]
-
Crick F. The
recent excitement about neural networks.
Nature. 1989;337:129–132.[Medline]
[Order article via Infotrieve]
-
Hinton G. How
neural networks learn from experience. Sci
Am. 1992;267:145–151.
-
Tu J.
Advantages and disadvantages of using artificial neural networks versus
logistic regression for predicting medical outcomes.
J Clin Epidemiol. 1996;49:1225–1231.[Medline]
[Order article via Infotrieve]
-
Guo Z,
Durand L, Lee H, et al. Artificial neural networks in computer assisted
classification of heart sounds in patients with porcine
bioprosthetic valves. Med Biol Eng
Comp. 1994;32:311–316.[Medline]
[Order article via Infotrieve]
-
Coehn M,
Hudson D. Comparative
Approaches to Medical
Reasoning. River Edge, NJ: World Scientific;
1995:271–288.
-
Binder T,
Sussner M, Moertl D, et al. Artificial neural networks: theoretical
considerations and clinical applicability.
Comput Biol Med. 1999;25:1069–1076.
-
Itchhaporia
D, Snow P, Almassy R, et al. Artificial neural networks: current status
in cardiovascular medicine.
J Am Coll Cardiol. 1996;28:515–521.[Abstract]
-
Silipo R,
Gori M, Taddei A, et al. Classification of arrhythmic events in
ambulatory elcetrocardiogram, using artificial neural networks.
Comput Biomed Res. 1995;28:305–318.[Medline]
[Order article via Infotrieve]
-
Dassen W,
Mulleneers R, DenDulk K, et al. An artificial neural network to
localize atrioventricular accessory pathways in
patients suffering from Wolfe-Parkinson-White syndrome.
Pacing Clin Electrophysiol. 1990;13:1792–1796.[Medline]
[Order article via Infotrieve]
-
Griffin M,
Scollan D, Moorman J. The dynamic range of neonatal heart rate
variability. J Cardiovasc
Electrophysiol. 1994;5:112–124.[Medline]
[Order article via Infotrieve]
-
Heden B,
Ohlsson M, Rittner R, et al. Agreement between artificial neural
networks and experienced electrocardiographer on electrocardiographic
diagnosis of healed myocardial infarction.
J Am Coll Cardiol. 1996;28:1012–1016.[Abstract]
-
Heden B,
Ohlsson M, Rittner R, et al. Acute myocardial infarction detected in
the 12 lead ECG by artificial neural networks.
Circulation. 1997;96:1798–1802.
[Abstract/Free Full Text]
-
Suzuki Y,
Ono K. Personal computer system for ECG ST-segment recognition based on
neural networks. Med Biol Eng
Comput. 1992;30:2–8.[Medline]
[Order article via Infotrieve]
-
Barschdorff
D, Femmer U, Trowitzsch E. Automatic phonocardiogram signal
analysis in infants based on wavelet transforms and artificial
neural networks. Comput
Cardiol. 1995;7:753–756.
-
Stremler
FG. Introduction to Communication
Systems. Reading, Mass: Addison-Wesley;
1977.
-
Masters T.
Practical Neural Networks in
C++. New York: Academic Press; 1993.
-
Rosner B.
Fundamentals of Biostatistics.
5th ed. New York: Duxbury Press; 2000.
-
Wartak J.
Phonocardiography: Integrated Study of
Heart Sounds and Murmurs. Hagerstown, Md: Harper & Row;
1972.
-
Obaidat M.
Phonocardiogram signal analysis: techniques and
performance comparison. J Med
Eng Technol. 1993;17:221–227.[Medline]
[Order article via Infotrieve]
-
Okuni M,
Hasimoto T, Kobayashi H, et al. Trial of a new cardiac mass screening
system in school children. Jpn Circ
J. 1978;42:49–52.[Medline]
[Order article via Infotrieve]
-
Reynolds
JL. Heart disease screening of preschool children.
Am J Dis Child. 1970;119:488–493.[Medline]
[Order article via Infotrieve]
-
White PR,
Collis WB, Salmon AP. Time frequency analysis of heart murmurs
in children. Proc IEEE. 1997;3:1–4.
-
El-Asir B,
Khadra L, Al-Abbasi AH. Time frequency analysis of heart
sounds. Proc IEEE. 1996;12:553–558.
-
Leung TS,
White PR, Collis WB, et al. Analysing pediatric heart murmurs with
discriminant analysis. Proc
IEEE. 1998;18:1628–1631.
-
Sarkady AA,
Clark RR, Williams R. Computer analysis techniques for
phonocardiogram diagnosis. Comput Biomed
Res. 1976;9:349–363.[Medline]
[Order article via Infotrieve]
-
Liang H,
Lukkarinen S, Hartimo I. Heart sound segmentation algorithm based on
heart sound envelogram. Proc
IEEE. 1997;24:105–108.
-
Bhatikar S,
Mahajan R. Neural network based diagnosis of CVD barrel reactor.
Adv Elec Pkg. 1999;1:621–626.
-
Marwah M,
Li Y, Mahajan R. Integrated neural network modeling for electronic
manufacturing. J Electronic
Manufacturing. 1996;6:79–91.
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