# Physionet Discovery Spider (`getdataforme/physionet-discovery-spider`) Actor The Physionet Discovery Spider is a web scraping tool for extracting biomedical signal data from PhysioNet. It allows customizable queries, flexible item limits, and ensures high-quality output with user-friendly configuration, aiding healthcare analytics and research.... - **URL**: https://apify.com/getdataforme/physionet-discovery-spider.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, Automation, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README --- ## README.md ### Physionet Discovery Spider #### Introduction The Physionet Discovery Spider is a powerful web scraping tool designed to extract biomedical signal data from the PhysioNet database. It enables users to efficiently gather and analyze large volumes of medical research data, facilitating advancements in healthcare analytics and research. #### Features - **Comprehensive Data Extraction**: Scrape detailed information on biomedical signals. - **Customizable Queries**: Tailor searches with specific keywords for targeted results. - **Flexible Item Limits**: Control the volume of data extracted per run. - **High-Quality Output**: Ensure reliable and accurate data collection. - **User-Friendly Configuration**: Simple setup with intuitive input parameters. #### Input Parameters | Parameter | Type | Required | Description | Example | |-------------|----------|----------|-----------------------------------------------------------------------------------------------|------------------| | Queries | array | No | The queries for the spider. Specify keywords to search within PhysioNet. | `["signals"]` | | item_limit | integer | No | Maximum items to scrape per actor run. Set to 0 for no limit. | `10` | #### Example Usage ##### Input JSON ```json { "Queries": ["signals"], "item_limit": 10 } ```` ##### Output JSON ```json [ { "keyword": "signals", "resource_type": "database", "access_type": "lock-open", "title": "Surface electromyographic signals collected during long-lasting ground walking of young able-bodied subjects", "url": "https://physionet.org/content/semg/1.0.1/", "authors": "Francesco Di Nardo, Christian Morbidoni, Sandro Fioretti", "description": "The dataset is composed of long-lasting surface electromyographic (sEMG) signals recorded from ten muscles during ground walking of 31 young able-bodied subjects in Movement Analysis Lab, Universit\u00e0 Politecnica delle Marche, Ancona, Italy.", "topics": [ "biomedical signals", "muscle recruitment", "surface emg signal", "walking", "gait analysis" ], "published": "Jan.", "version": "1.0.1", "visualize_url": null, "actor_id": "BG6y3LenBAALUglXc", "run_id": "dhOskMnQUGtQa9B67" }, { "keyword": "signals", "resource_type": "database", "access_type": "lock-open", "title": "Brno University of Technology ECG Signal Database with Annotations of P Wave (BUT PDB)", "url": "https://physionet.org/content/but-pdb/1.0.0/", "authors": "Lucie Mar\u0161\u00e1nov\u00e1, Andrea Nemcova, Radovan Smisek, et al.", "description": "BUT PDB is an ECG signal database with marked peaks of P waves created for the development, and objective comparison of P wave detection algorithms. The database consists of 50 2-minute 2-lead ECG signal records with various types of pathology.", "topics": [ "p wave", "ecg" ], "published": "Jan.", "version": "1.0.1", "visualize_url": null, "actor_id": "BG6y3LenBAALUglXc", "run_id": "dhOskMnQUGtQa9B67" }, { "keyword": "signals", "resource_type": "code", "access_type": "lock-open", "title": "Model for Simulating ECG and PPG Signals with Arrhythmia Episodes", "url": "https://physionet.org/content/ecg-ppg-simulator-arrhythmia/1.3.1/", "authors": "Andrius Solo\u0161enko, Andrius Petr\u0117nas, Birut\u0117 Paliakait\u0117, et al.", "description": "A model is capable of simulating sinus rhythm, atrial fibrillation and ectopic beats in ECGs and PPGs as well as extreme bradycardia and ventricular tachycardia in PPGs. Different types of noises and artifacts can also be added to the waveforms.", "topics": [ "arrhythmia", "atrial fibrillation", "noise", "tachycardia", "detection", "motion artifacts", "simulation", "bradycardia", "ppg", "ecg" ], "published": "Jan.", "version": "1.0.1", "visualize_url": null, "actor_id": "BG6y3LenBAALUglXc", "run_id": "dhOskMnQUGtQa9B67" } ] ``` #### Use Cases - **Market Research and Analysis**: Gather data for competitive analysis in the biomedical field. - **Competitive Intelligence**: Monitor advancements and publications by competitors. - **Price Monitoring**: Track pricing trends of medical devices and services. - **Content Aggregation**: Compile comprehensive datasets for research purposes. - **Academic Research**: Support studies with extensive biomedical signal data. - **Business Automation**: Automate data collection processes for efficiency. #### Installation and Usage 1. Search for "Physionet Discovery Spider" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters as needed. 4. Click "Start" to begin extraction. 5. Monitor progress in the log. 6. Export results in your preferred format (JSON, CSV, Excel). #### Output Format The output is a JSON array containing objects with the following fields: - `keyword`: The search term used. - `resource_type`: Type of resource (e.g., database, code). - `access_type`: Access level (e.g., lock-open). - `title`: Title of the resource. - `url`: URL to access the resource. - `authors`: List of authors. - `description`: Brief description of the resource. - `topics`: Relevant topics or tags. - `published`: Publication date. - `version`: Version number. - `visualize_url`: URL for visualization (if available). - `actor_id`: Unique identifier for the actor. - `run_id`: Unique identifier for the run. #### Error Handling The spider is designed to handle common web scraping errors such as timeouts and missing elements. If an error occurs, it will be logged in the Apify Console for review. Users can adjust settings or contact support for persistent issues. #### Rate Limiting and Best Practices To avoid being blocked by PhysioNet, adhere to rate limiting guidelines: - Implement delays between requests. - Respect `robots.txt` directives. - Use a reasonable number of concurrent requests. #### Limitations and Considerations - The spider is limited to publicly accessible data on PhysioNet. - Ensure compliance with PhysioNet's terms of service when using the spider. - Custom queries may require adjustments for optimal results. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: [Get Data For Me](https://getdataforme.com/contact/) We're here to help you get the most out of this Actor! # Actor input Schema ## `Queries` (type: `array`): The queries for the spider. ## `item_limit` (type: `integer`): Maximum items to scrape per actor run. Set to 0 for no limit. ## Actor input object example ```json { "Queries": [ "signals" ], "item_limit": 10 } ``` # Actor output Schema ## `results` (type: `string`): Scraped data items from dataset # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/physionet-discovery-spider").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("getdataforme/physionet-discovery-spider").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call getdataforme/physionet-discovery-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/physionet-discovery-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Physionet Discovery Spider", "description": "The Physionet Discovery Spider is a web scraping tool for extracting biomedical signal data from PhysioNet. It allows customizable queries, flexible item limits, and ensures high-quality output with user-friendly configuration, aiding healthcare analytics and research....", "version": "0.0", "x-build-id": "mDNt5MiFSfUXy2QTR" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~physionet-discovery-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-physionet-discovery-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~physionet-discovery-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-physionet-discovery-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~physionet-discovery-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-physionet-discovery-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "Queries": { "title": "Queries", "type": "array", "description": "The queries for the spider.", "default": [ "signals" ], "items": { "type": "string" } }, "item_limit": { "title": "Item limit", "minimum": 0, "type": "integer", "description": "Maximum items to scrape per actor run. Set to 0 for no limit.", "default": 10 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```