Investigations show that millions of people all around the world die as the result of sudden cardiac death (SCD). These deaths can be reduced by using medical equipment, such as defibrillators, after detection. We need to propose suitable ways to assist doctors to predict sudden cardiac death with a high level of accuracy. To do this, Linear, Time-Frequency (TF) and Nonlinear features have been extracted from HRV of ECG signal. Finally, healthy people and people at risk of SCD are classified by k-Nearest Neighbor (k-NN) and Multilayer Perceptron Neural Network (MLP). To evaluate, we have compared the classification rates for both separate and combined Nonlinear and TF features. The results show that HRV signals have special features in the vicinity of the occurrence of SCD that have the ability to distinguish between patients prone to SCD and normal people. We found that the combination of Time-Frequency and Nonlinear features have a better ability to achieve higher accuracy. The experimental results show that the combination of features can predict SCD by the accuracy of 99.73%, 96.52%, 90.37% and 83.96% for the first, second, third and forth one-minute intervals, respectively, before SCD occurrence.

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A Novel Approach to Predict Sudden Cardiac Death (SCD) Using Nonlinear and Time-Frequency Analyses from HRV Signals

As a 2-year project of the Japanese Society of Nuclear Medicine working group activity, normal myocardial imaging databases were accumulated and summarized. Stress-rest with gated and non-gated image sets were accumulated for myocardial perfusion imaging and could be used for perfusion defect scoring and normal left ventricular (LV) function analysis. For single-photon emission computed tomography (SPECT) with multi-focal collimator design, databases of supine and prone positions and computed tomography (CT)-based attenuation correction were created. The CT-based correction provided similar perfusion patterns between genders. In phase analysis of gated myocardial perfusion SPECT, a new approach for analyzing dyssynchrony, normal ranges of parameters for phase bandwidth, standard deviation and entropy were determined in four software programs. Although the results were not interchangeable, dependency on gender, ejection fraction and volumes were common characteristics of these parameters. Standardization of (123)I-MIBG sympathetic imaging was performed regarding heart-to-mediastinum ratio (HMR) using a calibration phantom method. The HMRs from any collimator types could be converted to the value with medium-energy comparable collimators. Appropriate quantification based on common normal databases and standard technology could play a pivotal role for clinical practice and researches.

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Normal values and standardization of parameters in nuclear cardiology: Japanese Society of Nuclear Medicine working group database

https://link.springer.com/content/pdf/10.1007%2Fs12350-015-0333-y.pdf

Comparison of phase dyssynchrony analysis using gated myocardial perfusion imaging with four software programs: Based on the Japanese Society of Nuclear Medicine working group normal database.

Artificial neural networks (ANN) might help to diagnose coronary artery disease. This study aimed to determine whether the diagnostic accuracy of an ANN-based diagnostic system and conventional quantitation are comparable. The ANN was trained to classify potentially abnormal areas as true or false based on the nuclear cardiology expert interpretation of 1001 gated stress/rest 99mTc-MIBI images at 12 hospitals. The diagnostic accuracy of the ANN was compared with 364 expert interpretations that served as the gold standard of abnormality for the validation study. Conventional summed stress/rest/difference scores (SSS/SRS/SDS) were calculated and compared with receiver operating characteristics (ROC) analysis. The ANN generated a better area under the ROC curves (AUC) than SSS (0.92 vs. 0.82, p  0.80. The ANN was diagnostically accurate in various clinical settings, including that of patients with previous myocardial infarction and coronary revascularization. The ANN could help to diagnose coronary artery disease.

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Diagnostic accuracy of an artificial neural network compared with statistical quantitation of myocardial perfusion images: a Japanese multicenter study.

/pdf/jcs-2018-guideline-on-diagnosis-of-chronic-coronary-heart-3m0yxuao61.pdf

JCS 2018 Guideline on Diagnosis of Chronic Coronary Heart Diseases.

Purpose
In patients with a small heart, defined as an end-systolic volume (ESV) of ≤20 mL calculated using the Quantitative Gated SPECT (QGS) program, underestimation of ESV and overestimation of ejection fraction (EF) using gated myocardial perfusion imaging are considered errors caused by inappropriate delineation of the left ventricle (LV). The aim of this study was to develop a new method for delineation of the LV and to evaluate it in studies using a digital phantom, normal subjects and patients.

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Improved quantification of small hearts for gated myocardial perfusion imaging

BACKGROUND The purpose of this study was to apply an artificial neural network (ANN) in patients with coronary artery disease (CAD) and to characterize its diagnostic ability compared with conventional visual and quantitative methods in myocardial perfusion imaging (MPI). METHODS AND RESULTS A total of 106 patients with CAD were studied with MPI, including multiple vessel disease (49%), history of myocardial infarction (27%) and coronary intervention (30%). The ANN detected abnormal areas with a probability of stress defect and ischemia. The consensus diagnosis based on expert interpretation and coronary stenosis was used as the gold standard. The left ventricular ANN value was higher in the stress-defect group than in the no-defect group (0.92±0.11 vs. 0.25±0.32, P<0.0001) and higher in the ischemia group than in the no-ischemia group (0.70±0.40 vs. 0.004±0.032, P<0.0001). Receiver-operating characteristics curve analysis showed comparable diagnostic accuracy between ANN and the scoring methods (0.971 vs. 0.980 for stress defect, and 0.882 vs. 0.937 for ischemia, both P=NS). The relationship between the ANN and defect scores was non-linear, with the ANN rapidly increased in ranges of summed stress score of 2-7 and summed defect score of 2-4. CONCLUSIONS Although the diagnostic ability of ANN was similar to that of conventional scoring methods, the ANN could provide a different viewpoint for judging abnormality, and thus is a promising method for evaluating abnormality in MPI.

Diagnostic Performance of Artificial Neural Network for Detecting Ischemia in Myocardial Perfusion Imaging

Technetium-labeled agents, which are most often used for assessing myocardial perfusion in myocardial perfusion scintigraphy (MPS), are cleared by the liver and excreted by the biliary system. Spillover from extra-cardiac activity into the myocardium, especially the inferior wall, might conceal defects and lower the diagnostic accuracy of the study. The objective was to determine rules of thumb for when reacquisition is useful due to high extra-cardiac uptake, i.e., when interpretation of the studies was affected by poor image quality. Patients admitted to MPS at any of the three study sites, who also underwent a reacquisition due to high extra-cardiac uptake were included. Image quality was assessed by ten technologists on a scale ranging from 1 to 5. Interpretations regarding the presence/absence of ischemia/infarction, including the certainty of the diagnosis, were made by three physicians. There was a statistically significant increase in image quality between the first and the repeated acquisition (1,256 cases of increased quality at the repeated study (66%), 134 cases of decreased quality at the repeated study (7%), 510 cases of unchanged quality (27%) P < 0.0001). The number of equivocal studies, interpreted by physicians, decreased when evaluating the repeated studies compared to the first studies for all physicians, both for the interpretations of ischemia and for infarction. Receiver operating characteristic analyses revealed that for both endpoints (ischemia, infarction) and all physicians, the optimal cutoff point for performing a reacquisition was between quality categories 2 and 3. This study indicates that repeat acquisition is useful when (1) the intensity of the extra-cardiac uptake is equal to or higher than the cardiac uptake when there is no separation between the extra-cardiac uptake and the inferior cardiac wall and (2) when the intensity of the extra-cardiac uptake is higher than the cardiac uptake when there is a separation between the extra-cardiac uptake and the inferior wall of less than one cardiac wall.

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When is reacquisition necessary due to high extra-cardiac uptake in myocardial perfusion scintigraphy?

Background: The European Society of Cardiology recommends that patients with >10% area of ischemia should receive revascularization. We investigated inter-observer variability for the extent of ischemic defects reported by different physicians and by different software tools, and if inter-observer variability was reduced when the physicians were provided with a computerized suggestion of the defects. Methods: Twenty-five myocardial perfusion single photon emission computed tomography (SPECT) patients who were regarded as ischemic according to the final report were included. Eleven physicians in nuclear medicine delineated the extent of the ischemic defects. After at least two weeks, they delineated the defects again, and were this time provided a suggestion of the defect delineation by EXINI Heart TM (EXINI). Summed difference scores and ischemic extent values were obtained from four software programs. Results: The median extent values obtained from the 11 physicians varied between 8% and 34%, and between 9% and 16% for the software programs. For all 25 patients, mean extent obtained from EXINI was 17.0% (± standard deviation (SD) 14.6%). Mean extent for physicians was 22.6% (± 15.6%) for the first delineation and 19.1% (± 14.9%) for the evaluation where they were provided computerized suggestion. Intra-class correlation (ICC) increased from 0.56 (95% confidence interval (CI) 0.41-0.72) to 0.81 (95% CI 0.71-0.90) between the first and the second delineation, and SD between physicians were 7.8 (first) and 5.9 (second delineation). Conclusions: There was large variability in the estimated ischemic defect size obtained both from different physicians and from different software packages. When the physicians were provided with a suggested delineation, the inter-observer variability decreased significantly.

/pdf/area-of-ischemia-assessed-by-physicians-and-software-1kmor9i5ud.pdf

Karin Nyström

Papers

Improved quantification of small hearts for gated myocardial perfusion imaging

Diagnostic Performance of Artificial Neural Network for Detecting Ischemia in Myocardial Perfusion Imaging

When is reacquisition necessary due to high extra-cardiac uptake in myocardial perfusion scintigraphy?

Area of ischemia assessed by physicians and software packages from myocardial perfusion scintigrams