| 1. |
- Sepehri, A. A., et al.
(författare)
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An Intelligent Phonocardiography for Automated Screening of Pediatric Heart Diseases
- 2016
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Ingår i: Journal of medical systems. - : Springer Science and Business Media LLC. - 0148-5598 .- 1573-689X. ; 40:1, s. 1-10
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a robust device for automated screening of pediatric heart diseases based on our unique processing method in murmur characterization; the Arash-Band method. The present study modifies the Arash-Band method and employs output of the modified method in conjunction with the two other original techniques to extract indicative feature vectors for the screening. The extracted feature vectors are classified by using the support vector machine method. Results show that the proposed modifications significantly enhances performance of the Arash-Band in terms of the both accuracy and sensitivity as the corresponding effect sizes are sufficiently large. The proposed algorithm has been incorporated into an Android-based tablet to constitute an intelligent phonocardiogram with the automatic screening capability. In order to obtain confidence interval of the accuracy and sensitivity, an inferable statistical test is applied on our database containing the phonocardiogram signals recorded from 263 of the referrals to a hospital. The expected value of the accuracy/sensitivity is estimated to be 87.45 % / 87.29 % with a 95 % confidence interval of (80.19 % – 92.47 %) / (76.01 % – 95.78 %) exhibiting superior performance than a pediatric cardiologist who relies on conventional or even computer-assisted auscultation.
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| 2. |
- Ghareh Baghi, Arash, et al.
(författare)
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An Edge Computing Method for Extracting Pathological Information from Phonocardiogram
- 2019
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Ingår i: Studies in Health Technology and Informatics. - : IOS Press. - 0926-9630 .- 1879-8365. - 9781614999867 ; 262, s. 364-367
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a structure of decision support system for pediatric cardiac disease, based on an Internet of Things (IoT) framework. The structure performs the intelligent decision making at its edge processing level, which classifies the heart sound signal, to three classes of cardiac conditions, normal, mild disease, and critical disease. Three types of the errors are introduced to evaluate the performance of the processing method, Type 1, 2 and 3, defined as the incorrect classification from the critical disease, mild, and normal, respectively. The method is validated using 140 real data patient records collected from the hospital referrals. The estimated negative errors for the Type 1, and 2, are calculated to be 0% and 4.8%, against the positive errors which are 6.3% and 13.3%, respectively. The Type 3, is calculated to be 16.7%, showing a high sensitivity of the method to be used in an IoT healthcare framework.
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| 3. |
- Ghareh Baghi, Arash, et al.
(författare)
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Distinguishing Aortic Stenosis from Bicuspid Aortic Valve in Children Using Intelligent Phonocardiography
- 2021
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Ingår i: IFMBE Proceedings. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783030646097 ; , s. 399-406
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Konferensbidrag (refereegranskat)abstract
- This paper presents a machine learning method to detect and discriminate between Aortic Stenosis (AS) and Bicuspid Aortic Valve (BAV) based on heart sound analysis. Differentiation between the two heart conditions is clinically important, but complicated if relying merely on the conventional auscultation. A novel form of the Time Growing Neural Network (TGNN) is introduced for the classification purpose. The method is applied to a dataset comprised of 87 children referrals to a university hospital, from which 50 individuals are healthy (with and without innocent murmur), and the rest are abnormal with either AS (15 individuals) or BAV (22 individuals). The baseline for comparison is a Time-Delayed Neural Network (TDNN) with the same size of the feature vector and the temporal frame. We used our original validation methods, named A-Test, which provides valuable information about structural risk and also learning capacity of any supervised classification method. A-Test is an elaborated version of K-Fold validation method, in a rather profound way. Performance of the TGNN is superior comparing to the presented TDNN, with an accuracy of 85.8% against 81.5%. This method can be integrated with our intelligent phonocardiography to serve as an enhanced assessment tool in hands of nurses or practitioners at primary healthcare centers.
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| 4. |
- Gharehbaghi, Arash, et al.
(författare)
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A machine learning method for screening children with patent ductus arteriosus using intelligent phonocardiography
- 2020
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Ingår i: EAI/Springer Innovations in Communication and Computing, 2020. - Cham : Springer. ; , s. 89-95
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Konferensbidrag (refereegranskat)abstract
- This paper presents a sophisticated machine learning method for screening children with patent ductus arteriosus (PDA) using the phonocardiogram recording as the input signal. The method is based on our original algorithm for finding the discriminative contents of the signal for the healthy children and the diseased ones, which we called Short Time Arash-Band (STAB) method. The STAB employs our specific discriminant analysis method for finding the joint temporal and spectral characteristics of the signal which provide the optimal segregation. Fifty pediatric referrals to a children university hospital, composed of 30 healthy and 20 with PDA, were participated in this study after obtaining the informed consent, according to the guidelines of the hospital which is in compliance with the declaration of Helsinki. The accuracy/sensitivity of the method was evaluated to be 86%/85%, using the leave-one-out validation method. Results show a potential for the approach in pediatric cardiac assessments that is considered as a demanding clinical application. Such an approach, which we called the intelligent phonocardiography, can be easily employed by the nurses or practitioners to improve the screening accuracy in the primary healthcare centers.
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| 5. |
- Gharehbaghi, Arash, et al.
(författare)
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A Novel Method for Screening Children with Isolated Bicuspid Aortic Valve
- 2015
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Ingår i: Cardiovascular Engineering and Technology. - : Springer Science and Business Media LLC. - 1869-408X .- 1869-4098. ; 6:4, s. 546-556
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a novel processing method for heart sound signal: the statistical time growing neural network (STGNN). The STGNN performs a robust classification by merging supervised and unsupervised statistical methods to overcome non-stationary behavior of the signal. By combining available preprocessing and segmentation techniques and the STGNN classifier, we build an automatic tool for screening children with isolated BAV, the congenital heart malformation which can lead to serious cardiovascular lesions. Children with BAV (22 individuals) and healthy condition (28 individuals) are subjected to the study. The performance of the STGNN is compared to that of a time growing neural network (CTGNN) and a conventional support vector (CSVM) machine, using balanced repeated random sub sampling. The average of the accuracy/sensitivity is estimated to be 87.4/86.5 for the STGNN, 81.8/83.4 for the CTGNN, and 72.9/66.8 for the CSVM. Results show that the STGNN offers better performance and provides more immunity to the background noise as compared to the CTGNN and CSVM. The method is implementable in a computer system to be employed in primary healthcare centers to improve the screening accuracy.
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| 6. |
- Gharehbaghi, Arash, et al.
(författare)
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An intelligent method for discrimination between aortic and pulmonary stenosis using phonocardiogram
- 2015
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Ingår i: IFMBE Proceedings. - Cham : Springer. - 9783319193878 ; , s. 1010-1013
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Konferensbidrag (refereegranskat)abstract
- This study presents an artificial intelligent-based method for processing phonocardiographic (PCG) signal of the patients with ejection murmur to assess the underlying pathology initiating the murmur. The method is based on our unique method for finding disease-related frequency bands in conjunction with a sophisticated statistical classifier. Children with aortic stenosis (AS), and pulmonary stenosis (PS) were the two patient groups subjected to the study, taking the healthy ones (no murmur) as the control group. PCG signals were acquired from 45 referrals to the children University hospital, comprised of 15 individuals of each group; all were diagnosed by the expert pediatric cardiologists according to the echocardiographic measurements together with the complementary tests. The accuracy of the method is evaluated to be 90% and 93.3% using the 5-fold and leave-one-out validation method, respectively. The accuracy is slightly degraded to 86.7% and 93.3% when a Gaussian noise with signal to noise ratio of 20 dB is added to the PCG signals, exhibiting an acceptable immunity against the noise. The method offered promising results to be used as a decision support system in the primary healthcare centers or clinics.
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| 7. |
- Ghareh Baghi, Arash, et al.
(författare)
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Extraction of diagnostic information from phonocardiographic signal using time-growing neural network
- 2019
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Ingår i: IFMBE Proceedings. - Singapore : Springer Verlag. ; , s. 849-853, s. 849-853
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Konferensbidrag (refereegranskat)abstract
- This paper presents an original method for extracting medical information from a heart sound recording, so called Phonocardiographic (PCG) signal. The extracted information is employed by a binary classifier to distinguish between stenosis and regurgitation murmurs. The method is based on using our original neural network, the Time-Growing Neural Network (TGNN), in an innovative way. Children with an obstruction on their semilunar valve are considered as the patient group (PG) against a reference group (RG) of children with a regurgitation in their atrioventricular valve. PCG signals were collected from 55 children, 25/30 from the PG/RG, who referred to the Children Medical Center of Tehran University. The study was conducted according to the guidelines of Good Clinical Practices and the Declaration of Helsinki. Informed consents were obtained for all the patients prior to the data acquisition. The accuracy and sensitivity of the method was estimated to be 85% and 80% respectively, exhibiting a very good performance to be used as a part of decision support system. Such a decision support system can improve the screening accuracy in primary healthcare centers, thanks to the innovative use of TGNN.
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| 8. |
- Ghareh Baghi, Arash, et al.
(författare)
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Forth heart sound detection using backward time-growing neural network
- 2020
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Ingår i: IFMBE Proceedings. - Cham : Springer Verlag. - 9783030179700 - 9783030179717 ; , s. 341-345
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Konferensbidrag (refereegranskat)abstract
- This paper presents a novel method for processing heart sound signal for screening forth heart sound (S4). The proposed method is based on time growing neural network with a new scheme, which we call the Backward Time-Growing Neural Network (BTGNN). The BTGNN is trained for detecting S4 in recordings of heart sound signal. In total, 83 children patients, referred to a children University hospital, participated in the study. The collected signals are composed of the subjects with and without S4 for training and testing the method. Performance of the method is evaluated using the Leave-One-Out and the repeated random sub sampling methods. The accuracy/sensitivity of the method is estimated to be 88.3%/82.4% and the structural risk is calculated to be 18.3% using repeated random sub sampling and the A-Test methods, respectively.
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| 9. |
- Sepehri, Amir A., et al.
(författare)
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A novel method for pediatric heart sound segmentation without using the ECG
- 2010
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Ingår i: Computer Methods and Programs in Biomedicine. - : Elsevier BV. - 0169-2607 .- 1872-7565. ; 99:1, s. 43-48
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we propose a novel method for pediatric heart sounds segmentation by paying special attention to the physiological effects of respiration on pediatric heart sounds. The segmentation is accomplished in three steps. First, the envelope of a heart sounds signal is obtained with emphasis on the first heart sound (Si) and the second heart sound (S(2)) by using short time spectral energy and autoregressive (AR) parameters of the signal. Then, the basic heart sounds are extracted taking into account the repetitive and spectral characteristics of Si and S2 sounds by using a Multi-Layer Perceptron (MLP) neural network classifier. In the final step, by considering the diastolic and systolic intervals variations due to the effect of a child's respiration, a complete and precise heart sounds end-pointing and segmentation is achieved.
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| 10. |
- Sepehri, Amir A., et al.
(författare)
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Computerized screening of children congenital heart diseases
- 2008
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Ingår i: Computerized screening of children congenital heart diseases CMPB. - United Kingdom : Elsevier BV. - 0169-2607. ; 92:2, s. 186-192
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we propose a method for automated screening of congenital heart diseases in children through heart sound analysis techniques. Our method relies on categorizing the pathological murmurs based on the heart sections initiating them. We show that these pathelogical murmur categories can be identified by examining the heart sound energy over specific frequency bands, which we call, Arash-Bands. To specify the Arash-Band for a category, we evaluate the energy of the heart sound over all possible frequency bands. The Arash-Band is the frequency band that provides the lowest error in clustering the instances of that category against the normal ones. The energy content of the Arash-Bands for different categories constitue a feature vector that is suitable for classification using a neural network. In order to train, and to evaluate the performance of the proposed method, we use a training data-bank, as well as a test data-bank, collectively consisting of ninety samples (normal and abnormal). Our results show that in more than 94% of cases, our method correctly identifies children with congenital heart diseases. This percentage improves to 100%, when we use the Jack-Knife validation method over all the 90 samples.
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| 11. |
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| 12. |
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| 13. |
- Ahrentorp, Fredrik, et al.
(författare)
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Sensitive magnetic biodetection using magnetic multi-core nanoparticles and RCA coils
- 2017
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Ingår i: Journal of Magnetism and Magnetic Materials. - : Elsevier BV. - 0304-8853 .- 1873-4766. ; 427, s. 14-18
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Tidskriftsartikel (refereegranskat)abstract
- We use functionalized iron oxide magnetic multi-core particles of 100 nm in size (hydrodynamic particle diameter) and AC susceptometry (ACS) methods to measure the binding reactions between the magnetic nanoparticles (MNPs) and bio-analyte products produced from DNA segments using the rolling circle amplification (RCA) method. We use sensitive induction detection techniques in order to measure the ACS response. The DNA is amplified via RCA to generate RCA coils with a specific size that is dependent on the amplification time. After about 75 min of amplification we obtain an average RCA coil diameter of about 1 mu m. We determine a theoretical limit of detection (LOD) in the range of 11 attomole (corresponding to an analyte concentration of 55 fM for a sample volume of 200 mu L) from the ACS dynamic response after the MNPs have bound to the RCA coils and the measured ACS readout noise. We also discuss further possible improvements of the LOD.
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| 14. |
- Ghareh Baghi, Arash, et al.
(författare)
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Method AND DEVICE FOR THE DETERMINATION OF MURMUR FREQUENCY BAND
- 2014
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Patent (populärvet., debatt m.m.)abstract
- The present invention is related to a method for the determination of frequency band characteristics of a heart disease. A first set of phonocardiograms are recorded from a first set of reference healthy patients, and a second set of phonocardiograms from a second set of patients suffering of a heart disease. Spectral energies of all possible frequency bands are then calculated. These spectral energies are then compared in order to determine an optimized frequency band that gives rise to the maximal distinction between spectral energies of the phonocardiograms from first and second set of phonocardiograms.
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| 15. |
- Gharehbaghi, Arash, et al.
(författare)
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A Hybrid Machine Learning Method for Detecting Cardiac Ejection Murmurs
- 2018
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Ingår i: EMBEC and NBC 2017. - Singapore : SPRINGER-VERLAG SINGAPORE PTE LTD. - 9789811051227 - 9789811051210 ; , s. 787-790
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Konferensbidrag (refereegranskat)abstract
- This paper presents a novel method for detecting cardiac ejection murmurs from other pathological and physiological heart murmurs in children. The proposed method combines a hybrid model and a time growing neural network for an improved detection even in mild condition. Children with aortic stenosis and pulmonary stenosis comprised the patient category against the reference category containing mitral regurgitation, ventricular septal defect, innocent murmur and normal (no murmur) conditions. In total, 120 referrals to a children University hospital participated to the study after giving their informed consent. Confidence interval of the accuracy, sensitivity and specificity is estimated to be 87.2%-88.8%, 83.4%-86.9% and 88.3%-90.0%, respectively.
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| 16. |
- Gharehbaghi, Arash, et al.
(författare)
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An Automatic Tool for Pediatric Heart Sounds Segmentation
- 2011
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Ingår i: Computing in Cardiology. vol. 28. - 9781457706127 ; , s. 37-40
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In this paper, we present a novel algorithm for pediatric heart sound segmentation, incorporated into a graphical user interface. The algorithm employs both the Electrocardiogram (ECG) and Phonocardiogram (PCG) signals for an efficient segmentation under pathological circumstances. First, the ECG signal is invoked in order to determine the beginning and end points of each cardiac cycle by using wavelet transform technique. Then, first and second heart sounds within the cycles are identified over the PCG signal by paying attention to the spectral properties of thesounds. The algorithm is applied on 120 recordings of normal and pathological children, totally containing 1976 cardiac cycles. The accuracy of thesegmentation algorithm is 97% for S1 and 94% for S2 identification while all the cardiac cycles are correctly determined.
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| 17. |
- Gharehbaghi, Arash, et al.
(författare)
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Distinguishing Septal Heart Defects from the Valvular Regurgitation Using Intelligent Phonocardiography
- 2020
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Ingår i: Digital Personalized Health and Medicine. - : IOS Press. - 9781643680828 - 9781643680835 ; 270, s. 178-182
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Konferensbidrag (refereegranskat)abstract
- This paper presents an original machine learning method for extracting diagnostic medical information from heart sound recordings. The method is proposed to be integrated with an intelligent phonocardiography in order to enhance diagnostic value of this technology. The method is tailored to diagnose children with heart septal defects, the pathological condition which can bring irreversible and sometimes fatal consequences to the children. The study includes 115 children referrals to an university hospital, consisting of 6 groups of the individuals: atrial septal defects (10), healthy children with innocent murmur (25), healthy children without any murmur (25), mitral regurgitation (15), tricuspid regurgitation (15), and ventricular septal defect (25). The method is trained to detect the atrial or ventricular septal defects versus the rest of the groups. Accuracy/sensitivity and the structural risk of the method is estimated to be 91.6%/88.4% and 9.89%, using the repeated random sub sampling and the A-Test method, respectively.
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| 18. |
- Gharehbaghi, Arash, et al.
(författare)
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Intelligent Phonocardiography for Screening Ventricular Septal Defect Using Time Growing Neural Network
- 2017
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Ingår i: INFORMATICS EMPOWERS HEALTHCARE TRANSFORMATION. - : IOS PRESS. - 9781614997818 - 9781614997801 ; , s. 108-111
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Konferensbidrag (refereegranskat)abstract
- This paper presents results of a study on the applicability of the intelligent phonocardiography in discriminating between Ventricular Spetal Defect (VSD) and regurgitation of the atrioventricular valves. An original machine learning method, based on the Time Growing Neural Network (TGNN), is employed for classifying the phonocardiographic recordings collected from the pediatric referrals to a children hospital. 90 individuals, 30 VSD, 30 with the valvular regurgitation, and 30 healthy subjects, participated in the study after obtaining the informed consents. The accuracy and sensitivity of the approach is estimated to be 86.7% and 83.3%, respectively, showing a good performance to be used as a decision support system.
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| 19. |
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| 20. |
- Miller, Rachael, et al.
(författare)
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Socio-ecological correlates of neophobia in corvids
- 2022
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Ingår i: Current Biology. - : Elsevier BV. - 0960-9822. ; 32:1, s. 4-85
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Tidskriftsartikel (refereegranskat)abstract
- Behavioral responses to novelty, including fear and subsequent avoidance of novel stimuli, i.e., neophobia, determine how animals interact with their environment. Neophobia aids in navigating risk and impacts on adaptability and survival. There is variation within and between individuals and species; however, lack of large-scale, comparative studies critically limits investigation of the socio-ecological drivers of neophobia. In this study, we tested responses to novel objects and food (alongside familiar food) versus a baseline (familiar food alone) in 10 corvid species (241 subjects) across 10 labs worldwide. There were species differences in the latency to touch familiar food in the novel object and novel food conditions relative to the baseline. Four of seven socio-ecological factors influenced object neophobia: (1) use of urban habitat (versus not), (2) territorial pair versus family group sociality, (3) large versus small maximum flock size, and (4) moderate versus specialized caching (whereas range, hunting live animals, and genus did not), while only maximum flock size influenced food neophobia. We found that, overall, individuals were temporally and contextually repeatable (i.e., consistent) in their novelty responses in all conditions, indicating neophobia is a stable behavioral trait. With this study, we have established a network of corvid researchers, demonstrating potential for further collaboration to explore the evolution of cognition in corvids and other bird species. These novel findings enable us, for the first time in corvids, to identify the socio-ecological correlates of neophobia and grant insight into specific elements that drive higher neophobic responses in this avian family group. Video abstract: [Figure presented]
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