Telemonitoring in subjects with newly diagnosed heart failure with reduced ejection fraction: From clinical research to everyday practice

© 2018, The Author(s) 2018. Introduction: Heart failure is increasingly common, and characterised by frequent admissions to hospital. To try and reduce the risk of hospitalisation, techniques such as telemonitoring (TM) may have a role. We wanted to determine if TM in patients with newly diagnosed heart failure and ejection fraction < 40% reduces the risk of readmission or death from any cause in a ‘real-world’ setting. Methods: This is a retrospective study of 124 patients (78.2% male; 68.6 ± 12.6 years) who underwent TM and 345 patients (68.5% male; 70.2 ± 10.7 years) who underwent the usual care (UC). The TM group were assessed daily by body weight, blood pressure and heart rate using electronic devices with automatic transfer of data to an online database. Follow-up was 12 months. Results: Death from any cause occurred in 8.1% of the TM group and 19% of the UC group (p = 0.002). There was no difference between the two groups in all-cause hospitalisation, either in the number of subjects hospitalised (p = 0.7) or in the number of admissions per patient (p = 0.6). There was no difference in the number of heart-failure-related readmissions per person between the two groups (p = 0.5), but the number of days in hospital per person was higher in the UC group (p = 0.03). Also, there were a significantly greater number of days alive and out of hospital for the patients in the TM group compared with the UC group (p = 0.0001). Discussion: TM is associated with lower any-cause mortality and also has the potential to reduce the number of days lost to hospitalisation and death

Prompt and accurate diagnosis of ventricular arrhythmias with a novel index based on phase space reconstruction of ECG

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Aim To develop a statistical index based on the phase space reconstruction (PSR) of the electrocardiogram (ECG) for the accurate and timely diagnosis of ventricular tachycardia (VT) and ventricular fibrillation (VF). Methods Thirty-two ECGs with sinus rhythm (SR) and 32 ECGs with VT/VF were analyzed using the PSR technique. Firstly, the method of time delay embedding were employed with the insertion of delay “τ” in the original time-series X(t), which produces the Y(t) = X(t − τ). Afterwards, a PSR diagram was reconstructed by plotting Y(t) against X(t). The method of box counting was applied to analyze the behavior of the PSR trajectories. Measures as mean (μ), standard deviation (σ) and coefficient of variation (CV = σ/μ), kurtosis (β) for the box counting of PSR diagrams were reported. Results During SR, CV was always 0.05. A similar pattern was observed with β, where < 6 was considered as the cut-off point between SR and VT/VF. Therefore, the upper threshold for SR was considered CVth = 0.05 and βth < 6. For optimisation of the accuracy, a new index (J) was proposed: J=wCVCVth+1−wββth. During SR the upper limit of J was the value of 1. Furthermore CV, β and J crossed the cut-off point timely before the onset of arrhythmia (average time: 4 min 31 s; SD: 2 min 30 s); allowing sufficient time for preventive therapy. Conclusion The J index improved ECG utility for arrhythmia monitoring and detection utility, allowing the prompt and accurate diagnosis of ventricular arrhythmias

A statistical index for early diagnosis of ventricular arrhythmia from the trend analysis of ECG phase-portraits

This is the author accepted manuscript. The final version is available from IOP Publishing via the DOI in this record.In this paper, we propose a novel statistical index for the early diagnosis of ventricular arrhythmia (VA) using the time delay phase-space reconstruction (PSR) technique, from the electrocardiogram (ECG) signal. Patients with two classes of fatal VA-with preceding ventricular premature beats (VPBs) and with no VPBs-have been analysed using extensive simulations. Three subclasses of VA with VPBs viz. ventricular tachycardia (VT), ventricular fibrillation (VF) and VT followed by VF are analyzed using the proposed technique. Measures of descriptive statistics like mean (µ), standard deviation (σ), coefficient of variation (CV = σ/µ), skewness (γ) and kurtosis (β) in phase-space diagrams are studied for a sliding window of 10 beats of the ECG signal using the box-counting technique. Subsequently, a hybrid prediction index which is composed of a weighted sum of CV and kurtosis has been proposed for predicting the impending arrhythmia before its actual occurrence. The early diagnosis involves crossing the upper bound of a hybrid index which is capable of predicting an impending arrhythmia 356 ECG beats, on average (with 192 beats standard deviation) before its onset when tested with 32 VA patients (both with and without VPBs). The early diagnosis result is also verified using a leave one out cross-validation (LOOCV) scheme with 96.88% sensitivity, 100% specificity and 98.44% accuracy.This work was supported by the E.U. ARTEMIS Joint Undertaking under the Cyclic and person-centric Health management: Integrated appRoach for hOme, mobile and clinical eNvironments—(CHIRON) Project, Grant Agreement # 2009-1-100228

A novel approach for the diagnosis of ventricular tachycardia based on phase space reconstruction of ECG

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record

Novel experimental and software methods for image reconstruction and localization in capsule endoscopy

Background and study aims: Capsule endoscopy (CE) is invaluable for minimally invasive endoscopy of the gastrointestinal tract; however, several technological limitations remain including lack of reliable lesion localization. We present an approach to 3D reconstruction and localization using visual information from 2D CE images. Patients and methods: Colored thumbtacks were secured in rows to the internal wall of a LifeLike bowel model. A PillCam SB3 was calibrated and navigated linearly through the lumen by a high-precision robotic arm. The motion estimation algorithm used data (light falling on the object, fraction of reflected light and surface geometry) from 2D CE images in the video sequence to achieve 3D reconstruction of the bowel model at various frames. The ORB-SLAM technique was used for 3D reconstruction and CE localization within the reconstructed model. This algorithm compared pairs of points between images for reconstruction and localization. Results: As the capsule moved through the model bowel 42 to 66 video frames were obtained per pass. Mean absolute error in the estimated distance travelled by the CE was 4.1 ± 3.9 cm. Our algorithm was able to reconstruct the cylindrical shape of the model bowel with details of the attached thumbtacks. ORB-SLAM successfully reconstructed the bowel wall from simultaneous frames of the CE video. The “track” in the reconstruction corresponded well with the linear forwards-backwards movement of the capsule through the model lumen. Conclusion: The reconstruction methods, detailed above, were able to achieve good quality reconstruction of the bowel model and localization of the capsule trajectory using information from the CE video and images alone

Fully convolutional neural networks for polyp segmentation in colonoscopy

Colorectal cancer (CRC) is one of the most common and deadliest forms of cancer, accounting for nearly 10% of all forms of cancer in the world. Even though colonoscopy is considered the most effective method for screening and diagnosis, the success of the procedure is highly dependent on the operator skills and level of hand-eye coordination. In this work, we propose to adapt fully convolution neural networks (FCN), to identify and segment polyps in colonoscopy images. We converted three established networks into a fully convolution architecture and fine-tuned their learned representations to the polyp segmentation task. We validate our framework on the 2015 MICCAI polyp detection challenge dataset, surpassing the state-of-the-art in automated polyp detection. Our method obtained high segmentation accuracy and a detection precision and recall of 73.61% and 86.31%, respectively

Towards a Computed-Aided Diagnosis System in Colonoscopy: Automatic Polyp Segmentation Using Convolution Neural Networks

Early diagnosis is essential for the successful treatment of bowel cancers including colorectal cancer (CRC), and capsule endoscopic imaging with robotic actuation can be a valuable diagnostic tool when combined with automated image analysis. We present a deep learning rooted detection and segmentation framework for recognizing lesions in colonoscopy and capsule endoscopy images. We restructure established convolution architectures, such as VGG and ResNets, by converting them into fully-connected convolution networks (FCNs), fine-tune them and study their capabilities for polyp segmentation and detection. We additionally use shape-from-shading (SfS) to recover depth and provide a richer representation of the tissue’s structure in colonoscopy images. Depth is incorporated into our network models as an additional input channel to the RGB information and we demonstrate that the resulting network yields improved performance. Our networks are tested on publicly available datasets and the most accurate segmentation model achieved a mean segmentation interception over union (IU) of 47.78% and 56.95% on the ETIS-Larib and CVC-Colon datasets, respectively. For polyp detection, the top performing models we propose surpass the current state-of-the-art with detection recalls superior to 90% for all datasets tested. To our knowledge, we present the first work to use FCNs for polyp segmentation in addition to proposing a novel combination of SfS and RGB that boosts performance

Nomenclature and semantic description of vascular lesions in small bowel capsule endoscopy: an international Delphi consensus statement

Background and study aims \u2002Nomenclature and descriptions of small bowel (SB) vascular lesions in capsule endoscopy (CE) are scarce in the medical literature. They are mostly based on the reader's opinion and thus differ between experts, with a potential negative impact on clinical care, teaching and research regarding SBCE. Our aim was to better define a nomenclature and to give a description of the most frequent vascular lesions in SBCE. Methods \u2002A panel of 18 European expert SBCE readers was formed during the UEGW 2016 meeting. Three experts constructed an Internet-based four-round Delphi consensus, but did not participate in the voting process. They built questionnaires that included various still frames of vascular lesions obtained with a third-generation SBCE system. The 15 remaining participants were asked to rate different proposals and description of the most common SB vascular lesions. A 6-point rating scale (varying from 'strongly disagree' to 'strongly agree') was used successive rounds. The consensus was reached when at least 80\u200a% voting members scored the statement within the 'agree' or 'strongly agree'. Results \u2002Consensual terms and descriptions were reached for angiectasia/angiodysplasia, erythematous patch, red spot/dot, and phlebectasia. A consensual description was reached for more subtle vascular lesions tentatively named "diminutive angiectasia" but no consensus was reached for this term. Conclusion \u2002An international group has reached a consensus on the nomenclature and descriptions of the most frequent and relevant SB vascular lesions in CE. These terms and descriptions are useful in daily practice, for teaching and for medical research purposes