Glottal flow characteristics in vowels produced by speakers with heart failure

Heart failure (HF) is one of the most life-threatening diseases globally. HF is an under-diagnosed condition, and more screening tools are needed to detect it. A few recent studies have suggested that HF also affects the functioning of the speech production mechanism by causing generation of edema in the vocal folds and by impairing the lung function. It has not yet been studied whether these possible effects of HF on the speech production mechanism are large enough to cause acoustically measurable differences to distinguish speech produced in HF from that produced by healthy speakers. Therefore, the goal of the present study was to compare speech production between HF patients and healthy controls by focusing on the excitation signal generated at the level of the vocal folds, the glottal flow. The glottal flow was computed from speech using the quasi-closed phase glottal inverse filtering method and the estimated flow was parameterized with 12 glottal parameters. The sound pressure level (SPL) was measured from speech as an additional parameter. The statistical analyses conducted on the parameters indicated that most of the glottal parameters and SPL were significantly different between the HF patients and healthy controls. The results showed that the HF patients generally produced a more rounded glottal pulse and a lower SPL level compared to the healthy controls, indicating incomplete glottal closure and inappropriate leakage of air through the glottis. The results observed in this preliminary study indicate that glottal features are capable of distinguishing speakers with HF from healthy controls. Therefore, the study suggests that glottal features constitute a potential feature extraction approach which should be taken into account in future large-scale investigations in studying the automatic detection of HF from speech.Peer reviewe

The automatic detection of heart failure using speech signals

Heart failure (HF) is a major global health concern and is increasing in prevalence. It affects the larynx and breathing - thereby the quality of speech. In this article, we propose an approach for the automatic detection of people with HF using the speech signal. The proposed method explores mel-frequency cepstral coefficient (MFCC) features, glottal features, and their combination to distinguish HF from healthy speech. The glottal features were extracted from the voice source signal estimated using glottal inverse filtering. Four machine learning algorithms, namely, support vector machine, Extra Tree, AdaBoost, and feed-forward neural network (FFNN), were trained separately for individual features and their combination. It was observed that the MFCC features yielded higher classification accuracies compared to glottal features. Furthermore, the complementary nature of glottal features was investigated by combining these features with the MFCC features. Our results show that the FFNN classifier trained using a reduced set of glottal + MFCC features achieved the best overall performance in both speaker-dependent and speaker-independent scenarios. (C) 2021 The Author(s). Published by Elsevier Ltd.Peer reviewe

ANGPTL8 protein-truncating variant associated with lower serum triglycerides and risk of coronary disease

Protein-truncating variants (PTVs) affecting dyslipidemia risk may point to therapeutic targets for cardiometabolic disease. Our objective was to identify PTVs that were associated with both lipid levels and the risk of coronary artery disease (CAD) or type 2 diabetes (T2D) and assess their possible associations with risks of other diseases. To achieve this aim, we leveraged the enrichment of PTVs in the Finnish population and tested the association of low-frequency PTVs in 1,209 genes with serum lipid levels in the Finrisk Study (n = 23,435). We then tested which of the lipid-associated PTVs were also associated with the risks of T2D or CAD, as well as 2,683 disease endpoints curated in the FinnGen Study (n = 218,792). Two PTVs were associated with both lipid levels and the risk of CAD or T2D: triglyceride-lowering variants in ANGPTL8 (-24.0[-30.4 to -16.9] mg/dL per rs760351239-T allele, P = 3.4 x 10(-9)) and ANGPTL4 (-14.4[-18.6 to -9.8] mg/dL per rs746226153-G allele, P = 4.3 x 10(-9)). The risk of T2D was lower in carriers of the ANGPTL4 PTV (OR = 0.70[0.60-0.81], P = 2.2 x 10(-6)) than noncarriers. The odds of CAD were 47% lower in carriers of a PTV in ANGPTL8 (OR = 0.53[0.37-0.76], P = 4.5 x 10(-4)) than noncarriers. Finally, the phenome-wide scan of the ANGPTL8 PTV showed that the ANGPTL8 PTV carriers were less likely to use statin therapy (68,782 cases, OR = 0.52[0.40-0.68], P = 1.7 x 10(-6)) compared to noncarriers. Our findings provide genetic evidence of potential long-term efficacy and safety of therapeutic targeting of dyslipidemias. Author summary Studying the health impacts of protein-truncating variants (PTVs) enables detecting the health impact of drugs that inhibit these same genes. Our study aimed to expand our knowledge of genes associated with cardiometabolic disease, along with the side effects of these genes. To detect PTVs associated with cardiometabolic disease, we first performed a genome-wide scan of PTVs associated with serum lipid levels in Finns. We found PTVs in two genes highly enriched in Finns, which were associated with both serum lipid levels and a lower risk of type 2 diabetes or coronary artery disease: ANGPTL4 and ANGPTL8. To evaluate the other health effects of these PTVs, we performed an association scan between the PTVs and 2,683 disease endpoints curated in the FinnGen Study (n = 218,792). We demonstrate that using human populations with PTV-enrichment, such as Finns, offers considerable boosts in statistical power to detect potential long-term efficacy and safety of pharmacologically targeting genes.Peer reviewe

Genetic predictors of lifelong medication-use patterns in cardiometabolic diseases

Peer reviewe

Comparative scores of our bouton detection algorithm versus EPBscore results.

<p>Comparative scores of our bouton detection algorithm versus EPBscore results.</p

Examples from the 3D axon dataset.

<p><b>A</b>, image with several crossing axons. <b>B</b>, image with 2 crossing axons with low intensity. <b>C</b>, image with high intensity noise (on the right). <b>D</b>, image with blob-like noise.</p

Example of the bouton detection method step by step.

<p><b>A</b>, A mean intensity projection image from the 3D axon dataset. <b>B</b>, The same image convolved with LoG mask. <b>C</b>, In this example Interest points were detected using SURF (green “+” signs). <b>D</b>, Following SVM classification, the final proposed boutons are plotted on the mean projection image (white “+” signs).</p

Examples of how True Positives (TP), False Positives (FP), False Negative (FN) and True Negatives (TN) were classified.

<p>For the calculation of these scores, we manually labelled boxes around the correct boutons. A point was classified as a TP only when its <i>x</i> and <i>y</i> coordinates lie within one of the boxes, and only 1 TP was counted per box (i.e. if there are 2 points within the box, 1 would be counted as a TP, and one would be counted as FP). FNs were classified for the number of detected boxes that did not have any points. The TNs were all the other points in the image (not including the 25 × 25 boxes around all other TPs, FPs, and FNs).</p

Flow chart of the bouton detection method.

<p>Our proposed algorithm has 5 main steps. (1) A negative Laplacian of Gaussian (LoG) mask is used in order to enhance blob-like objects (i.e. boutons) in the mean intensity projected image. (2) An interest point detector then detects the possible bouton locations. (3) Non-maximum suppression is used to move candidate boutons to their local maxima, and removes multiple detections of the same bouton in a close local area. (4) Feature vectors (with 12 elements each) are then generated at the location of the detected interest points. (5) A trained SVM classifies the points as boutons or non-boutons. (6) The last step uses the 2D coordinates to define a search volume for the 3rd coordinate.</p

Graphs comparing the performance of the descriptors and interest point detectors at 10³ SVM class thresholds.

<p>We chose Gabor and SURF, as our descriptor and interest point detector, as they had better performance than the other methods (all with separately optimized hyperparameters). The precision-recall graphs seem to have an unusual curvature; however, this can be explained by the nature of the dataset. In this axon dataset, where the number of TPs (i.e. boutons) is relatively small compared to the size of the image, it is to be expected that there will always be some FP detections when TP points are also detected. As such, there will never be a case in which precision = 1, as there will always be some FPs detected as well (i.e. the SVM can not have a FPR of 0). <b>A</b>, Precision-Recall curve comparing feature descriptors (AUC: Gabor = 0.779, HOG = 0.728, SIFT = 0.75). Gabor based descriptors reached the highest Precision, and has the best overall performance, demonstrated by the AUC. <b>B</b>, Precision-Recall curve comparing interest point detectors (AUC: SURF = 0.779, Harris = 0.598, SIFT = 0.357). SURF reaches the best TPR in comparison to the other methods. <b>C</b>, ROC curve comparing feature descriptors (AUC: Gabor = 1.8 × 10⁻⁵, HOG = 1.65 × 10⁻⁵, SIFT = 1.49 × 10⁻⁵). Gabor has the best overall performance, demonstrated by the AUC. <b>D</b>, ROC curve comparing interest point detectors (AUC: SURF = 1.8 × 10⁻⁵, Harris = 1.08 × 10⁻⁵, SIFT = 4.69 × 10⁻⁶). SURF reaches the best Recall in comparison to the other methods. <b>E-F</b>, Error bar graphs comparing metrics between the descriptors and interest point detectors, respectively. Gabor and SIFT have the best overall performance across the metrics compared. The dotted lines are where the graphs saturate. TPR, True Positive Rate; FPR, False Positive Rate; FP, False Positive; TP, True Positive; Error bars, SEM; AUC, Area Under Curve.</p