Background
Localization of atrioventricular accessory pathways (AP) from Electrocardiogram (ECG) is crucial for successful ablation. We analyzed the value of limb lead 2 versus 3 QRS vector discordance on surface ECG among right‐sided pathways.
Methods
Data from consecutive patients undergoing successful ablation of manifest AP were analyzed. They were categorized into two groups—Gr I: Endocardial ablation from anterior and anterolateral tricuspid annulus (TA, 10−1 o'clock, right anterolateral [RAL]); Gr II: Ablation outside this region (1−10 o'clock of TA). Inferior lead discordance (ILD) was defined as positive QRS complex (monophasic R, Rs) in lead 2 with negative/equiphasic QRS vector in lead 3 (rS, S, RS). Maximally pre‐excited ECGs during electrophysiology study were compared for presence of ILD.
Result
Among total 22 cases (Age 36 ± 18 years, 12 males), ILD was noted in 4/4 cases of Gr I. It was absent among 17/18 cases of right‐sided AP in Gr II. The only case in Gr II having ILD was ablated near 8 o'clock (posterolateral). In contrast to the other four cases, aVF was negative, along with lead 3. A close differential was mid‐septal AP (MSAP). However, the MSAP had absence of r in V1 and lead 2 having rS/RS complex in contrast to strongly positive QRS in RAL pathways. The sensitivity and specificity of ILD for RAL are 100% and 95%, respectively. The positive, negative predictive value, and accuracy are 80%, 100%, and 95%, respectively.
Conclusion
Positive QRS complex in lead 2 with negative QRS in lead 3 in maximally pre‐excited ECG is often predictive of anterior and anterolateral location among right‐sided pathways.
Regional language extraction from a natural scene image is always a challenging proposition due to its dependence on the text information extracted from Image. Text Extraction on the other hand varies on different lighting condition, arbitrary orientation, inadequate text information, heavy background influence over text and change of text appearance. This paper presents a novel unified method for tackling the above challenges. The proposed work uses an image correction and segmentation technique on the existing Text Detection Pipeline an Efficient and Accurate Scene Text Detector (EAST). EAST uses standard PVAnet architecture to select features and non maximal suppression to detect text from image. Text recognition is done using combined architecture of MaxOut convolution neural network (CNN) and Bidirectional long short term memory (LSTM) network. After recognizing text using the Deep Learning based approach, the native Languages are translated to English and tokenized using standard Text Tokenizers. The tokens that very likely represent a location is used to find the Global Positioning System (GPS) coordinates of the location and subsequently the regional languages spoken in that location is extracted. The proposed method is tested on a self generated dataset collected from Government of India dataset and experimented on Standard Dataset to evaluate the performance of the proposed technique. Comparative study with a few state-of-the-art methods on text detection, recognition and extraction of regional language from images shows that the proposed method outperforms the existing methods.
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