{"id":15008755,"url":"https://github.com/jocrv/library-ekg-philips","last_synced_at":"2026-03-14T09:01:32.221Z","repository":{"id":209687142,"uuid":"724706980","full_name":"jocrv/Library-EKG-Philips","owner":"jocrv","description":null,"archived":false,"fork":false,"pushed_at":"2023-11-28T16:49:30.000Z","size":9,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-19T02:51:19.265Z","etag":null,"topics":["python2"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jocrv.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-11-28T16:21:19.000Z","updated_at":"2023-11-28T18:16:17.000Z","dependencies_parsed_at":null,"dependency_job_id":"f0683514-0444-4be1-b0d7-5f84a59918f2","html_url":"https://github.com/jocrv/Library-EKG-Philips","commit_stats":null,"previous_names":["jocrv/lib-metrics-iam"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jocrv%2FLibrary-EKG-Philips","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jocrv%2FLibrary-EKG-Philips/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jocrv%2FLibrary-EKG-Philips/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jocrv%2FLibrary-EKG-Philips/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jocrv","download_url":"https://codeload.github.com/jocrv/Library-EKG-Philips/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243204626,"owners_count":20253415,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["python2"],"created_at":"2024-09-24T19:20:22.988Z","updated_at":"2025-12-24T09:50:54.221Z","avatar_url":"https://github.com/jocrv.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# lib-EKG\n\n\nPhilips ECG Wave Detection Library\nOverview\nThis library is a comprehensive tool developed to facilitate the analysis and detection of waves on ECG (Electrocardiogram) echocardiogram data obtained from Philips Machines. The library provides a set of functions and utilities to process and extract meaningful information from ECG signals, with a primary focus on identifying and determining various waves in the signal.\n\nFeatures\n1. Wave Detection\nThe library includes robust algorithms for detecting key waves in an ECG signal, such as:\n\nP-waves: Identifying atrial depolarization.\nQRS-complex: Detecting ventricular depolarization.\nT-waves: Capturing ventricular repolarization.\nThese detection algorithms are designed to handle diverse signal variations and noise commonly encountered in real-world ECG data.\n\n2. Data Preprocessing\nTo enhance the accuracy of wave detection, the library includes preprocessing functions to clean and filter ECG signals. This involves noise reduction, baseline correction, and other techniques to improve signal quality.\n\n3. Visualization\nThe library provides visualization tools for ECG signals, allowing users to observe the detected waves alongside the original signal. This aids in validation and fine-tuning of the detection parameters.\n\nUsage:\n\nimport philips_ecg_wave_detection as ecg\n\n# Load ECG data from a Philips Machine\necg_data = ecg.load_data(\"path/to/ecg_file.philips\")\n\n# Preprocess the ECG data\npreprocessed_data = ecg.preprocess(ecg_data)\n\n# Detect waves in the ECG signal\ndetected_waves = ecg.detect_waves(preprocessed_data)\n\n# Visualize the results\necg.plot_results(ecg_data, detected_waves)\n\n\nRefer to the documentation for detailed usage instructions and additional functionalities.\n\nDocumentation\nFor detailed documentation and examples, visit inside this repository.\n\nContributions\nContributions to the library are welcome! If you find bugs, have feature requests, or want to contribute code, please check our Contribution Guidelines.\n\nLicense\nThis library is licensed under the MIT License.\n\nAcknowledgments\nWe would like to express our gratitude to Philips for providing valuable insights and support during the development of this library.\n\n\n\n\n-Python 2.7\n\nPython 2.7, there are a few adjustments you need to make in terms of syntax and library compatibility. Below is a modified version of the README file specifically tailored for Python 2.7.\n\n# Philips ECG Wave Detection Library\n\n## Overview\n\nThis library is a comprehensive tool developed to facilitate the analysis and detection of waves on ECG (Electrocardiogram) echocardiogram data obtained from Philips Machines. The library provides a set of functions and utilities to process and extract meaningful information from ECG signals, with a primary focus on identifying and determining various waves in the signal.\n\n## Features\n\n### 1. Wave Detection\n\nThe library includes robust algorithms for detecting key waves in an ECG signal, such as:\n\n- **P-waves:** Identifying atrial depolarization.\n- **QRS-complex:** Detecting ventricular depolarization.\n- **T-waves:** Capturing ventricular repolarization.\n\nThese detection algorithms are designed to handle diverse signal variations and noise commonly encountered in real-world ECG data.\n\n### 2. Data Preprocessing\n\nTo enhance the accuracy of wave detection, the library includes preprocessing functions to clean and filter ECG signals. This involves noise reduction, baseline correction, and other techniques to improve signal quality.\n\n### 3. Visualization\n\nThe library provides visualization tools for ECG signals, allowing users to observe the detected waves alongside the original signal. This aids in validation and fine-tuning of the detection parameters.\n\n## Getting Started\n\n### Installation\n\nTo install the library, use the following pip command:\n\n```bash\npip install philips_ecg_wave_detection\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjocrv%2Flibrary-ekg-philips","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjocrv%2Flibrary-ekg-philips","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjocrv%2Flibrary-ekg-philips/lists"}