This article explores the concept of schizophrenia as a collapse of coordination and smoothness in brain function. The absence of murmuration leads to symptoms such as illogical verbal function, social disconnectednes...This article explores the concept of schizophrenia as a collapse of coordination and smoothness in brain function. The absence of murmuration leads to symptoms such as illogical verbal function, social disconnectedness, and inappropriate responses to environmental demands. Current treatment options are limited to electroshock therapy and medications like Clozaril, which have significant drawbacks. Future potential cures may involve genetic engineering, but this approach poses social, philosophical, and moral challenges.展开更多
A coronary-pulmonary artery fistula with giant aneurysmal dilatation is an extremely rare clinical constellation.The natural course of this disease and the incidence of complications are unknown.Hence,optimal treatmen...A coronary-pulmonary artery fistula with giant aneurysmal dilatation is an extremely rare clinical constellation.The natural course of this disease and the incidence of complications are unknown.Hence,optimal treatment,particularly in asymptomatic patients,is still a matter of debate.Here we report a case of a 71-year-old asymptomatic woman with a diastolic murmur.Comprehensive cardiovascular assessments including cardiac computed tomography and invasive coronary angiography revealed a coronary-pulmonary artery fi stula with giant aneurysmal dilatation.The patient was managed conservatively and has now been followed up for 5 years without any events.展开更多
Background: Dynamic subaortic stenosis occurs in differing situations, commonly with hypertrophic cardiomyopathy. Regardless of the underlying cause, the resulting murmurs usually possess a characteristic sound spectr...Background: Dynamic subaortic stenosis occurs in differing situations, commonly with hypertrophic cardiomyopathy. Regardless of the underlying cause, the resulting murmurs usually possess a characteristic sound spectral pattern, manifesting a sharp and high frequency peak occurring late in systole, often bearing a striking resemblance to the subaortic Doppler flow pattern. Methods: Murmurs found in thirty one subjects with dynamic subaortic stenosis were analyzed after having been recorded with a novel portable device capable of spectral and waveform sound displays. Results: All subjects manifested characteristic frequency patterns, consisting of high and sharp peaks occurring in late systole. With significant subaortic stenosis (resting subaortic flow velocity > 2 m/sec) this pattern was evident at rest. In the presence of little or no resting subaortic obstruction (< 2 m/sec) this pattern was produced regularly by the Valsalva maneuver. Conclusions: Dynamic subaortic stenosis produces a specific sound spectral pattern that may provide a basis for clinical evaluation, especially in early detection of this disorder and in screening situations.展开更多
Heart murmur recognition and classification play an important role in the auscultative diagnosis. The method based on hidden markov model (HMM) was presented to recognize the heart murmur. The murmur was isolated on b...Heart murmur recognition and classification play an important role in the auscultative diagnosis. The method based on hidden markov model (HMM) was presented to recognize the heart murmur. The murmur was isolated on basis of the principle of wavelet analysis considering the time-frequency characteristics of the heart murmur. This method uses Mel frequency cepstral coefficient (MFCC) to extract representative features and develops hidden Markov model (HMM) for signal classification. The result shows that this method?is able to recognize the murmur efficiently and superior to BP?neural network (94.2% vs 82.8%). And the findings suggest that the method may have the potential to be used to assist doctors for a more objective diagnosis.展开更多
In this paper, a novel framework based on a time-frequency (TF) approach is proposed for detection of murmurs from heart sound signal. First, a high-resolution TF algorithm, matching pursuit, was used to decompose eac...In this paper, a novel framework based on a time-frequency (TF) approach is proposed for detection of murmurs from heart sound signal. First, a high-resolution TF algorithm, matching pursuit, was used to decompose each heart beat into a series of TF atoms selected from a redundant dictionary. Next, representative components of murmurs were identified by clustering the selected atoms of all the beats into a finite number of clusters. Then, Wigner-Ville distribution of the representative components was used to generate a set of 8 features which were fed to a classifier. Experiments with a dataset consisting of heart sounds from 35 normal and 35 pathological subjects showed a classification accuracy of 95.71% in distinguishing murmurs from normal heart sounds.展开更多
In the effort to enhance cardiovascular diagnostics,deep learning-based heart sound classification presents a promising solution.This research introduces a novel preprocessing method:iterative k-means clustering combi...In the effort to enhance cardiovascular diagnostics,deep learning-based heart sound classification presents a promising solution.This research introduces a novel preprocessing method:iterative k-means clustering combined with silhouette score analysis,aimed at downsampling.This approach ensures optimal cluster formation and improves data quality for deep learning models.The process involves applying k-means clustering to the dataset,calculating the average silhouette score for each cluster,and selecting the clusterwith the highest score.We evaluated this method using 10-fold cross-validation across various transfer learningmodels fromdifferent families and architectures.The evaluation was conducted on four datasets:a binary dataset,an augmented binary dataset,amulticlass dataset,and an augmentedmulticlass dataset.All datasets were derived from the Heart Wave heart sounds dataset,a novelmulticlass dataset introduced by our research group.To increase dataset sizes and improve model training,data augmentation was performed using heartbeat cycle segmentation.Our findings highlight the significant impact of the proposed preprocessing approach on the HeartWave datasets.Across all datasets,model performance improved notably with the application of our method.In augmented multiclass classification,the MobileNetV2 model showed an average weighted F1-score improvement of 27.10%.In binary classification,ResNet50 demonstrated an average accuracy improvement of 8.70%,reaching 92.40%compared to its baseline performance.These results underscore the effectiveness of clustering with silhouette score analysis as a preprocessing step,significantly enhancing model accuracy and robustness.They also emphasize the critical role of preprocessing in addressing class imbalance and advancing precision medicine in cardiovascular diagnostics.展开更多
文摘This article explores the concept of schizophrenia as a collapse of coordination and smoothness in brain function. The absence of murmuration leads to symptoms such as illogical verbal function, social disconnectedness, and inappropriate responses to environmental demands. Current treatment options are limited to electroshock therapy and medications like Clozaril, which have significant drawbacks. Future potential cures may involve genetic engineering, but this approach poses social, philosophical, and moral challenges.
文摘A coronary-pulmonary artery fistula with giant aneurysmal dilatation is an extremely rare clinical constellation.The natural course of this disease and the incidence of complications are unknown.Hence,optimal treatment,particularly in asymptomatic patients,is still a matter of debate.Here we report a case of a 71-year-old asymptomatic woman with a diastolic murmur.Comprehensive cardiovascular assessments including cardiac computed tomography and invasive coronary angiography revealed a coronary-pulmonary artery fi stula with giant aneurysmal dilatation.The patient was managed conservatively and has now been followed up for 5 years without any events.
文摘Background: Dynamic subaortic stenosis occurs in differing situations, commonly with hypertrophic cardiomyopathy. Regardless of the underlying cause, the resulting murmurs usually possess a characteristic sound spectral pattern, manifesting a sharp and high frequency peak occurring late in systole, often bearing a striking resemblance to the subaortic Doppler flow pattern. Methods: Murmurs found in thirty one subjects with dynamic subaortic stenosis were analyzed after having been recorded with a novel portable device capable of spectral and waveform sound displays. Results: All subjects manifested characteristic frequency patterns, consisting of high and sharp peaks occurring in late systole. With significant subaortic stenosis (resting subaortic flow velocity > 2 m/sec) this pattern was evident at rest. In the presence of little or no resting subaortic obstruction (< 2 m/sec) this pattern was produced regularly by the Valsalva maneuver. Conclusions: Dynamic subaortic stenosis produces a specific sound spectral pattern that may provide a basis for clinical evaluation, especially in early detection of this disorder and in screening situations.
文摘Heart murmur recognition and classification play an important role in the auscultative diagnosis. The method based on hidden markov model (HMM) was presented to recognize the heart murmur. The murmur was isolated on basis of the principle of wavelet analysis considering the time-frequency characteristics of the heart murmur. This method uses Mel frequency cepstral coefficient (MFCC) to extract representative features and develops hidden Markov model (HMM) for signal classification. The result shows that this method?is able to recognize the murmur efficiently and superior to BP?neural network (94.2% vs 82.8%). And the findings suggest that the method may have the potential to be used to assist doctors for a more objective diagnosis.
文摘In this paper, a novel framework based on a time-frequency (TF) approach is proposed for detection of murmurs from heart sound signal. First, a high-resolution TF algorithm, matching pursuit, was used to decompose each heart beat into a series of TF atoms selected from a redundant dictionary. Next, representative components of murmurs were identified by clustering the selected atoms of all the beats into a finite number of clusters. Then, Wigner-Ville distribution of the representative components was used to generate a set of 8 features which were fed to a classifier. Experiments with a dataset consisting of heart sounds from 35 normal and 35 pathological subjects showed a classification accuracy of 95.71% in distinguishing murmurs from normal heart sounds.
基金supported by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,under grant No.IPP:533-611-2025DSR technical and financial support.
文摘In the effort to enhance cardiovascular diagnostics,deep learning-based heart sound classification presents a promising solution.This research introduces a novel preprocessing method:iterative k-means clustering combined with silhouette score analysis,aimed at downsampling.This approach ensures optimal cluster formation and improves data quality for deep learning models.The process involves applying k-means clustering to the dataset,calculating the average silhouette score for each cluster,and selecting the clusterwith the highest score.We evaluated this method using 10-fold cross-validation across various transfer learningmodels fromdifferent families and architectures.The evaluation was conducted on four datasets:a binary dataset,an augmented binary dataset,amulticlass dataset,and an augmentedmulticlass dataset.All datasets were derived from the Heart Wave heart sounds dataset,a novelmulticlass dataset introduced by our research group.To increase dataset sizes and improve model training,data augmentation was performed using heartbeat cycle segmentation.Our findings highlight the significant impact of the proposed preprocessing approach on the HeartWave datasets.Across all datasets,model performance improved notably with the application of our method.In augmented multiclass classification,the MobileNetV2 model showed an average weighted F1-score improvement of 27.10%.In binary classification,ResNet50 demonstrated an average accuracy improvement of 8.70%,reaching 92.40%compared to its baseline performance.These results underscore the effectiveness of clustering with silhouette score analysis as a preprocessing step,significantly enhancing model accuracy and robustness.They also emphasize the critical role of preprocessing in addressing class imbalance and advancing precision medicine in cardiovascular diagnostics.
