Before discussing the confusing aspects of terminology used to describe back problems, it is important to first review the overall terminology used to describe the normal anatomy of the spine. Watch: Spine Anatomy Overview Video. Vertebrae are also sometimes called vertebral bodies. See Cervical Spine Anatomy. See Lumbar Spine Anatomy and Pain. Disorders are common in the lumbar spine and at the top of the sacral region, as this area supports most of the body's weight which creates stress on the structures in this area.
The combination of these two sections of the lower back is often referred to as the " lumbosacral region ". It is applied to assess the structure as where The slope vectors are calculated for covering area values across scales. After the estimation, the local directions are summed, forming the result which yields the information related to the structure of primary heart sounds.
Similar approaches based on summing the local directions are used in image processing for different structures in the shape context methods [ 26 , 27 ].
In our approach, the sum of local directions, described by 9a and 9b , is applied for heart sound identification.
Each of the methods 7 , 8a , 8b , 9a , and 9b can give insight into the content and shape of the structures. Computation error in the multiscale structure estimation does not produce significant consequences on the proposed methods suitability. The computation error, such as an error of the roundoff noise nature, affects the upper and lower areas, and in a similar way; thus the overall impact is negligible. The classification method is based on support vector machine SVM classifier, which is considered as a suitable tool for discrimination tasks [ 28 — 30 ].
Namely, SVM is applied as a classifier which distinguishes the data by finding a separating hyperplane with a maximal margin between the classes. When applied to the waveforms, it is described by the kernel function and regularization parameter, based on the trade-off having large normalized margin and less constraint violation.
The kernel function is used to train the SVM, where the most common kernel types are the linear and the Gaussian radial basis function RBF described by its squared bandwidth [ 21 , 30 ].
SVM based classification is performed using fivefold cross-validation [ 28 ], where nine hundred sound sequences are used. The separation of the candidates is made during the cross-validation to properly estimate the overall performance, where the classification is performed without any prior knowledge, meaning that the sequences used in the training phase are not a part of the dataset used for testing. The recursive feature elimination technique is used to improve the classification accuracy by eliminating the least significant descriptors [ 29 , 30 ].
The ROC curve presents true positive rate versus false positive rate for different decision thresholds, where as a performance measure the Area Under the Curve AUC is calculated. Moreover, the classification accuracy and -measure are calculated as respectively, where are the true positives S1 hits , are the true negatives S2 hits , are the false positives missed S2 , and are the false negatives missed S1. The -measure describes the class imbalance. The three proposed methods, described in Section 2.
They are compared to additional methods from the literature. We also considered statistical and shape related methods, based on kurtosis and skewness [ 4 , 8 ]. In Figure 3 , the positive area and the negative area are calculated in accordance with the sound amplitude, while the total area 1 and the total area 2 are calculated using the sound amplitude and the Shannon energy based envelope, respectively.
The results presented in Figure 3 show that all three methods proposed in Section 2. Note that our proposed methods are related to shape characterization. Examples of some hits and missed candidates for a set of waveforms using the third proposed method, described by 9a and 9b , are presented in Figure 4 , where only a few candidates are misinterpreted due to their structures.
The tests with AUC performance are followed by SVM based classification and cross-validation, where the selection of methods is made using the feature elimination and the grid search technique [ 28 — 30 ]. In order to obtain robust results in sound characterization, the accuracies are calculated after five repetitions dividing the recordings in a random manner. For SVM based classification, we analyzed all previous methods which had been tested individually.
For the classification, different number of descriptors is used in the feature elimination technique. For the case , the best result is obtained for the two proposed shape context methods and , where a decision boundary is presented in Figure 5 a. For different values of N , the obtained accuracy and AUC values are presented in Figure 5 b showing the noticeable changes in accuracy in comparison to AUC performance. This is mainly due to the third method which is shown as the most significant one among the tested methods for the waveform characterization.
The best performance is found for , where the selected descriptors are the proposed shape context values and , BFD2, and the total area calculated as In this case the best accuracy results are obtained and presented in Table 1. The proposed SVM based classification utilizes the adaptive filtering and the measuring areas for the sound classification.
In this paper, the cross-validation is performed only according to the healthy pediatric subjects. An additional validation of the model is performed on the waveforms belonging to ten patients which were not included in the cross-validation procedure, where the proposed structure assessment methodology showed excellent results with AUC of The study in this paper is applied and tested for the primary heart sound identification process on the basis of shape related characterization for pediatric subjects.
The advantage of the proposed methodology relies on the applied adaptive filtering and the selected structure assessment. The high accuracy results for the classification are obtained efficiently, without time-consuming characterization methods, by employing the shape context characterization and keeping a small number of descriptors. The applied method also overcomes high iterations for the calculation. Thus, applying the filtering towards the structure enables adapting to the most prominent extreme points.
It can be noticed that the S1 adapting to the structure encounters the higher number of the prominent local extremes than in S2. The limitations of the proposed methodology are directly related to the sound characterization.
In particular, the misclassified sounds are found among the missed examples presented in Figure 4. These are the limitations related to the found positions of the most prominent maximum and minimum used for clamping the envelopes, where some side details may produce the misinterpretation. The proposed model is adjusted to the healthy individuals. The additional experiment for nonhealthy group using a set of waveforms is performed under the same circumstances providing high accuracy results.
The study is based on the shape context characterization and can be considered valuable for automatic heart sound analysis. In comparison to the identification from [ 5 , 11 ], where highest envelope value is applied for S1-systole and S2-diastole differentiation, the structure assessment overcomes the errors found due to varying energy in a signal, as presented in Figure 6 a. Recurring sounds are not assumed for the classification model, and thus the methodology may overcome errors found due to nondetected candidates and similar misinterpretations.
The obtained ROC curves are presented in Figure 6 b with 6. The study in this paper analyzes the possibility of using the shape context and fractal theory in the S1 and S2 pattern characterization. The fractal theory based approaches enable developing new methods keeping a small number of descriptors in the identification of the primary sounds.
The study shows the significance of the shape context and ability to differentiate the sounds regardless of the variable energy values without even considering intersound relationships. Moreover, the obtained results indicate that the shape related approaches are valuable for further improvements in the identification of the heart sounds. The authors declare that there are no conflicts of interest regarding the publication of this article. This research has been partially supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia under Grant no.
The decreased pressure allows more blood to flow abnormally through the ASD from the high pressured left atrium to the right atrium, ultimately resulting again in increased flow through the pulmonic valve — again, delaying its closure. The S3 sound is actually produced by the large amount of blood striking a very compliant LV.
Enlarge If the LV is not overly compliant, as is in most adults, a S3 will not be loud enough to be auscultated. A S3 can be a normal finding in children, pregnant females and well-trained athletes; however, a S4 heart sound is almost always abnormal. A S3 can be an important sign of systolic heart failure because, in this setting, the myocardium is usually overly compliant, resulting in a dilated LV; this can be seen in the image below.
Enlarge Normal LV vs. Dilated LV S3 Present. S3 is a low-pitched sound; this is helpful in distinguishing a S3 from a split S2, which is high pitched. A S3 heart sound should disappear when the diaphragm of the stethoscope is used and should be present while using the bell; the opposite is true for a split S2. Also, the S3 sound is heard best at the cardiac apex, whereas a split S2 is best heard at the pulmonic listening post left upper sternal border.
To best hear a S3, the patient should be in the left lateral decubitus position. If the LV is noncompliant, and atrial contraction forces blood through the atrioventricular valves, a S4 is produced by the blood striking the LV. Therefore, any condition that creates a noncompliant LV will produce a S4, while any condition that creates an overly compliant LV will produce a S3, as described above.
A S4 heart sound can be an important sign of diastolic HF or active ischemia and is rarely a normal finding. Diastolic HF frequently results from severe left ventricular hypertrophy, or LVH , resulting in impaired relaxation compliance of the LV.
In this setting, a S4 is often heard. Also, if an individual is actively having myocardial ischemia, adequate adenosine triphosphate cannot be synthesized to allow for the release of myosin from actin; therefore, the myocardium is not able to relax, and a S4 will be present. It is important to note that if a patient is experiencing atrial fibrillation, the atria are not contracting, and it is impossible to have a S4 heart sound.
Like S3, the S4 sound is low pitched and best heard at the apex with the patient in the left lateral decubitus position. Below is comparative information for S3 and S4.
There are a few common extra heart sounds that the clinician may encounter. Systolic ejection click: A systolic ejection click frequently indicates a bicuspid aortic valve. This sound is heard just after the S1 heart sound. Usually, the opening of the aortic valve is not audible; however, with a bicuspid aortic valve, the leaflets dome suddenly prior to opening and create a systolic ejection click.
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