Sex Recognition through ECG Signals aiming toward Smartphone Authentication

Lopez, Jose-Luis Cabra; Parra, Carlos; Gomez, Libardo; Trujillo, Luis Carlos

doi:10.3390/app12136573

Cited by 12 publications

(9 citation statements)

References 38 publications

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“…In the eld of natural language processing, LSTM model performs well in language modelling, machine translation, semantic analysis and other tasks. In the teaching of English writing, Cabra Lopez et al (2022) began to explore the application of LSTM model to the task of syntactic analysis, in order to improve learners' syntactic understanding and writing ability [14]. The advantage of LSTM model is that it shows better performance when dealing with long text sequences [15].…”

Section: Literature Reviewmentioning

confidence: 99%

Syntactic Analysis of SMOSS Model Combined with Improved LSTM Model: Taking English Writing Teaching as an Example

Yan

2023

Preprint

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In order to improve learners' syntactic understanding and writing ability, thus effectively improving the quality of English writing teaching, this paper explores the method of combining Sequential Matching on Sliding Window Sequences (SMOSS) model with improved Long Short-Term Memory (LSTM) model in English writing teaching to improve the effect of syntactic analysis. Firstly, this paper analyzes the structure of SMOSS model. Secondly, this paper optimizes the traditional LSTM model by using Connectist Temporal Classification (CTC), and proposes an English text error detection model. Meanwhile, this paper combines the SMOSS model with the optimized LSTM model to form a comprehensive syntactic analysis framework, and designs and implements the structure and code of the framework. Finally, on the one hand, the semantic disambiguation performance of the model is tested by using SemCor data set. On the other hand, taking English writing teaching as an example, the proposed method is further verified by designing a comparative experiment in groups. The results show that: (1) From the experimental data of word sense disambiguation, the accuracy of the SMOSS-LSTM model proposed in this paper is the lowest when the context range is "3 + 3", then it rises in turn at "5 + 5" and "7 + 7", reaches the highest at "7 + 7", and then begins to decrease at "10 + 10"; (2) Compared with the control group, the accuracy of syntactic analysis in the experimental group reached 89.5%, while that in the control group was only 73.2%. (3) In the aspect of English text error detection, the detection accuracy of the proposed model in the experimental group is as high as 94.8%, which is significantly better than the traditional SMOSS-based text error detection method, and its accuracy is only 68.3%. This paper verifies the effectiveness and practicability of applying SMOSS model and improved LSTM model to the syntactic analysis task in English writing teaching, and provides new ideas and methods for the application of syntactic analysis in English teaching.

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Section: Literature Reviewmentioning

confidence: 99%

Syntactic Analysis of SMOSS Model Combined with Improved LSTM Model: Taking English Writing Teaching as an Example

Yan

2023

Preprint

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“…These approaches have improved five common applications, namely business intelligence, access control, image filtering, soft biometrics [13], and health-related services [14], as well as supporting IoT solutions [15]. Nevertheless, recent studies have pointed out the possibility of sex recognition through the electric signal of the heart (electrocardiogram), with a significant classification rate [16][17][18], emphasizing neural network techniques. We propose such a recognition approach with the intention of it being used for device user authentication [19] or in a cardiac context [16].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, recent studies have pointed out the possibility of sex recognition through the electric signal of the heart (electrocardiogram), with a significant classification rate [16][17][18], emphasizing neural network techniques. We propose such a recognition approach with the intention of it being used for device user authentication [19] or in a cardiac context [16].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, as evidenced by our previous research [16], the architecture of this work had the following characteristics: We extended the signal analysis, which usually takes place with subjects in the resting position, because our database contained a 24-hour record. In fact, although we did not control the person's stance variable, we achieved significant ECG sex differentiation.…”

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confidence: 99%

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A Fast Deep Learning ECG Sex Identifier Based on Wavelet RGB Image Classification

Lopez

Parra

Buitrago

2023

Data

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Human sex recognition with electrocardiogram signals is an emerging area in machine learning, mostly oriented toward neural network approaches. It might be the beginning of a field of heart behavior analysis focused on sex. However, a person’s heartbeat changes during daily activities, which could compromise the classification. In this paper, with the intention of capturing heartbeat dynamics, we divided the heart rate into different intervals, creating a specialized identification model for each interval. The sexual differentiation for each model was performed with a deep convolutional neural network from images that represented the RGB wavelet transformation of ECG pseudo-orthogonal X, Y, and Z signals, using sufficient samples to train the network. Our database included 202 people, with a female-to-male population ratio of 49.5–50.5% and an observation period of 24 h per person. As our main goal, we looked for periods of time during which the classification rate of sex recognition was higher and the process was faster; in fact, we identified intervals in which only one heartbeat was required. We found that for each heart rate interval, the best accuracy score varied depending on the number of heartbeats collected. Furthermore, our findings indicated that as the heart rate increased, fewer heartbeats were needed for analysis. On average, our proposed model reached an accuracy of 94.82% ± 1.96%. The findings of this investigation provide a heartbeat acquisition procedure for ECG sex recognition systems. In addition, our results encourage future research to include sex as a soft biometric characteristic in person identification scenarios and for cardiology studies, in which the detection of specific male or female anomalies could help autonomous learning machines move toward specialized health applications.

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“…9 To improve point of care contact, several smartphonebased healthcare technologies have been developed in form of applications and hardware devices in recent years that has increased patient access to timely care and diagnosis, these technologies include ECG hardware and smartphone application for easy acquisition of 12 lead ECGs to detect arrhythmia and CAD. [10][11][12] A prototype named Spandan 12 L ECG, a single channel ECG has been developed as a smartphone ECG system. It has the potential of 12-lead recordings acquisition based on the Goldberg ECG system or standard 12-lead ECG system.…”

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confidence: 99%

Validation of interpretation of ST-elevation myocardial infarction using the smartphone based 12 lead electrocardiograms in comparison to the cardiologist-a cross sectional study, validation study, single-center study

Mahajan¹,

Garg²,

Sharma³

et al. 2023

Int J Clin Trials

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Background: In patients experiencing an ST-elevation myocardial infarction (STEMI), rapid diagnosis leads to optimal clinical outcomes. Recent technology has provided access to a reliable means of obtaining an ECG reading through a smartphone application (app) that works with an attachment providing all 12-leads of a standard ECG system. We developed a 12-lead smartphone-based electrocardiogram (ECG) called “Spandan”, and an application to accurately assess the presence of STEMI in patients presenting with chest pain. Objectives of study was to perform clinical observation studies on the patients screened for STEMIs and evaluate the variation in the diagnosis of STEMIs detected in both 12-lead gold standard and Spandan 12 lead ECG. Methods: This single-center study was carried out at Shri Mahant Indresh hospital (SMIH), Dehradun, Uttarakhand, India from April-2022 to November-2022. All patients (n=278) visiting the ECG room with symptoms of chest pain, shortness of breath and past history of cardiovascular disease at the department of cardiology of the SMIH, Dehradun were included in the study. Results: Mean age (SD) was 53.96±15.31 years. The male gender participants was 75.17%. 14.3% were STEMI patients as per the standard 12 lead interpretation, smartphone ECG interpreted 7.1% of the participants as STEMI and cardiologist interpreted nearly 15.4% of the participants of STEMI. Conclusions: This study confirmed the potential of a smartphone ECG for evaluation of STEMI and the feasibility of studying this technology further to define the diagnostic accuracy, limitations, and appropriate use of this new technology.

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Sex Recognition through ECG Signals aiming toward Smartphone Authentication

Cited by 12 publications

References 38 publications

Syntactic Analysis of SMOSS Model Combined with Improved LSTM Model: Taking English Writing Teaching as an Example

Syntactic Analysis of SMOSS Model Combined with Improved LSTM Model: Taking English Writing Teaching as an Example

A Fast Deep Learning ECG Sex Identifier Based on Wavelet RGB Image Classification

Validation of interpretation of ST-elevation myocardial infarction using the smartphone based 12 lead electrocardiograms in comparison to the cardiologist-a cross sectional study, validation study, single-center study

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