LINQ II Insertable Cardiac Monitor, Zelda AI ECG Classification System
K210484 · Medtronic, Inc. · MXD · Jun 11, 2021 · Cardiovascular
Device Facts
Record ID
K210484
Device Name
LINQ II Insertable Cardiac Monitor, Zelda AI ECG Classification System
Applicant
Medtronic, Inc.
Product Code
MXD · Cardiovascular
Decision Date
Jun 11, 2021
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 870.1025
Device Class
Class 2
Attributes
AI/ML, PCCP
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Atrial Fibrillation Detection
Deep-learning neural architectures, residual convolutional network and ensemble models
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Pause Detection
Deep-learning neural architectures, residual convolutional network and ensemble models
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Indications for Use
The LINQ II ICM is an insertable automatically-activated and patient-activated monitoring system that records subcutaneous ECG and is indicated in the following cases: patients with clinical syndromes or situations at increased risk of cardiac arrhythmias patients who experience transient symptoms such as dizziness, palpitation, syncope, and chest pain that may suggest a cardiac arrhythmia
Device Story
LINQ II ICM is an insertable, subcutaneous cardiac monitor recording ECG; monitors for tachyarrhythmia, bradyarrhythmia, pause, atrial tachyarrhythmia, and atrial fibrillation. System includes Zelda AI ECG Classification System for automated arrhythmia detection. Patients use MyCareLink Heart App or Patient Assistant to manually record symptoms. Device continuously senses subcutaneous ECG; Zelda AI processes signals to classify cardiac rhythms and reduce false alerts. Output provided to clinicians for arrhythmia diagnosis and management. Benefits include improved detection of transient cardiac events and reduced false positive alerts. Used in clinical settings or home environments; operated by patients and clinicians.
Clinical Evidence
Bench testing and design validation only. No clinical trial data presented. Validation focused on algorithm performance (Zelda AI) to reduce false alerts for AF and Pause episodes while retaining true alerts. All results met or exceeded pre-defined acceptance criteria.
Technological Characteristics
Hybrid sapphire substrate; titanium foil antenna/electrodes; sputtered titanium nitride electrode coating; lithium anode (SVO/CFx) battery. Small form factor. Connectivity via mobile app/patient assistant. Software algorithm: deep-learning neural architecture (residual convolutional network and ensemble models). Standards: ISO 14971:2019, ISO 15223-1:2016, IEC 82304-1:2016, IEC 62304:2006/AMD 1:2015, ANSI/AAMI EC57:2012, AAMI/ANSI 60601-1:2005 A1:2012.
Indications for Use
Indicated for patients with clinical syndromes/situations at increased risk of cardiac arrhythmias or those experiencing transient symptoms (dizziness, palpitation, syncope, chest pain) suggesting cardiac arrhythmia. Not tested for pediatric use.
Regulatory Classification
Identification
The arrhythmia detector and alarm device monitors an electrocardiogram and is designed to produce a visible or audible signal or alarm when atrial or ventricular arrhythmia, such as premature contraction or ventricular fibrillation, occurs.
Special Controls
*Classification.* Class II (special controls). The guidance document entitled “Class II Special Controls Guidance Document: Arrhythmia Detector and Alarm” will serve as the special control. See § 870.1 for the availability of this guidance document.
Predicate Devices
LINQ II Insertable Cardiac Monitor, Model LNQ22 (K200795)
Submission Summary (Full Text)
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June 11, 2021
Medtronic, Inc. Dianna Johannson Distinguished Regulatory Affairs Advisor 8200 Coral Sea Street NE Mounds View, Minnesota 55112
Re: K210484
Trade/Device Name: LINQ II Insertable Cardiac Monitor, Zelda AI ECG Classification System Regulation Number: 21 CFR 870.1025 Regulation Name: Arrhythmia detector and alarm (including ST-segment measurement and alarm) Regulatory Class: Class II Product Code: MXD Dated: May 11, 2021 Received: May 12, 2021
Dear Dianna Johannson:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's
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requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (OS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Jennifer Shih Kozen Assistant Director Division of Cardiac Electrophysiology, Diagnostics and Monitoring Devices Office of Cardiovascular Devices Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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#### Indications for Use
510(k) Number (if known) K210484
Device Name
LINQ II Insertable Cardiac Monitor (Model LNQ22)
Indications for Use (Describe)
The LINQ II ICM is an insertable automatically-activated and patient-activated monitoring system that records subcutaneous ECG and is indicated in the following cases:
• patients with clinical syndromes or situations at increased risk of cardiac arrhythmias
· patients who experience transient symptoms such as dizziness, palpitation, syncope, and chest pain that may suggest a cardiac arrhythmia
The device has not been tested specifically for pediatric use.
| Type of Use (Select one or both, as applicable) | |
|-------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------|
| <div> <span> <span style="font-size:16px">✔</span> Prescription Use (Part 21 CFR 801 Subpart D) </span> </div> | <div> <span> <span style="font-size:16px">☐</span> Over-The-Counter Use (21 CFR 801 Subpart C) </span> </div> |
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K210484 Page 1 of 4
# 510(k) Summary
| Date Prepared: | February 16, 2021 | |
|--------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--|
| Submitter: | Medtronic, Inc.<br>Cardiovascular Diagnostics and Services<br>8200 Coral Sea Street NE<br>Mounds View, MN 55112<br>Establishment Registration Number: 2182208 | |
| Contact Person: | Dianna L Johannson<br>Distinguished Regulatory Affairs Advisor<br>Cardiovascular Diagnostics and Services<br>Phone: (763) 526-2376<br>Fax: (651) 367-0603<br>Email: dianna.johannson@medtronic.com | |
| Alternate Contact: | Ryan Calabrese<br>Sr Regulatory Affairs Director<br>Cardiovascular Diagnostics and Services<br>Phone: (763) 526-3515<br>Fax: (651) 367-0603<br>Email: ryan.s.calabrese@medtronic.com | |
## General Information
| Trade Name: | LINQ TM II |
|-----------------------|-------------------------------------------------------------------------------|
| Common Name: | Insertable Cardiac Monitor |
| Regulation Number: | CFR 870.1025 |
| Product Code: | MXD |
| Classification: | Class II |
| Classification Panel: | Cardiovascular |
| Special Controls: | Class II Special Controls Guidance Document: Arrhythmia Detector and<br>Alarm |
| Predicate Device: | LINQ II Insertable Cardiac Monitor, Model LNQ22 (K200795) |
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### Device Description
The LINQ II Insertable Cardiac Monitor (ICM) Model LNQ22 is a programmable device that continuously monitors a patient's ECG and other physiological parameters. The device records cardiac information in response to automatically detected arrhythmias and patient-initiated activation or markings. The device is designed to automatically record the occurrence of an episode of arrhythmia in a patient. Note: Arrhythmias are classified as tachyarrhythmia, bradyarrhythmia, pause, atrial tachyarrhythmia, or atrial fibrillation. Patients may also manually record symptoms. In order to manually record symptoms, the patient will also need either the MyCareLink Heart App (patient app on mobile device) or the Patient Assistant Model PA97000. The patient can use the MyCareLink Heart App or the Patient to manually record his or her cardiac rhythm while experiencing or immediately after a symptomatic event. LINQ II ICM includes the following accessories: LINQ Tool Kit Model LNQ22TK, Reveal LINQTM Mobile Manager Model MSW002, Device Command Library Model 2692, and Instrument Command Library Model 2691. New to the LINO II ICM system is the Zelda AI ECG Classification System Models ZA400. ZA410. ZA420. included in this submission.
#### Indications for Use
The LINQ II ICM Indications for Use remains the same as a result of this submission and are as follows:
The LINQ II ICM is an insertable automatically-activated and patient-activated monitoring system that records subcutaneous ECG and is indicated in the following cases:
- patients with clinical syndromes or situations at increased risk of cardiac arrhythmias ●
- patients who experience transient symptoms such as dizziness, palpitation, syncope, ● and chest pain that may suggest a cardiac arrhythmia
The device has not been tested specifically for pediatric use.
### Technological Characteristics
The LINQ II ICM consists of a hybrid substrate that is made of sapphire. The sapphire provides part of the implantable hermetic enclosure, integrates the feedthroughs directly into the substrate, and provides a substrate for component attachment/interconnect. The antenna and sense electrodes are titanium foil laser bonded to the sapphire substrate and connected directly to the embedded feedthroughs. The sense electrodes are coated with sputtered titanium nitride. The sapphire is laser bonded to the titanium battery cover, which provides the complete hermetic enclosure. The battery is Lithium anode, silver vanadium oxide/carbon monofluoride cathode with a capacity of 167 mAh.
The LINO II ICM will continue to use the same technology. It is designed to automatically record the occurrence of an arrhythmia in a patient, continuously sense the patient's subcutaneous ECG, and analyze the timing of ventricular events to detect possible episodes of arrhythmia. The LINQ II ICM has a small form factor, and uses Sapphire, Titanium, Parylene, and Titanium Nitride coating on the sensing electrodes as body contacting materials.
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When compared to the predicate LINQ II ICM (K200795), the LINQ II ICM when used with the Zelda AI ECG Classification System has the same Indications for Use, operating principle, device technology and functionality, and biological safety.
When compared to the predicate LINQ II ICM (K200795), the LINQ II ICM differs only in its use with the Zelda AI ECG Classification System.
#### Substantial Equivalence
Differences between the subject and predicate devices have been evaluated through bench testing to provide evidence of safe and effective use. The LINQ II ICM when used with the Zelda AI ECG Classification System is substantially equivalent to the predicate LINQ II ICM (K200795) based on comparisons of indications for use, operating principle, device technology and functionality, and safety.
### Summary of Testing
Design verification and design validation were performed to demonstrate that the LINQ II ICM when used with the Zelda AI ECG Classification System met both design requirements and established performance criteria to support substantial equivalence to the predicate LINO II ICM (K200795).
- . Design Verification: Software design verification was completed to ensure the design output meets specifications outlined in the design inputs. The Zelda ECG Classification System meets the functionality per the requirements and all test executions resulted in a status of Passed.
- Design Validation: Performance validation testing and analysis were completed to ensure the algorithms were able to reduce false alerts from ICM detected AF and Pause episodes while retaining true alerts. All results met or exceeded the criteria in the Validation Plan.
Since there were no changes to the LINQ II ICM itself, there was no development or testing specific to the ICM; therefore, no standards are referenced for the LINQ II ICM.
The following standards were used for development and testing of the Zelda AI ECG Classification System.
| Standard<br>Number | Standard<br>Organization | Recognition<br>Number | Standard Title |
|--------------------|--------------------------|-----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------|
| 14971:2019 | ISO | 5-125 | Medical Devices - Application of Risk<br>Management to Medical Devices |
| 15223-1:2016 | ISO | 5-117 | Medical devices - Symbols to be used with<br>medical device labels, labelling, and<br>information to be supplied - Part 1: General<br>requirements |
| 82304-1:2016 | IEC | 13-97 | Health software - Part 1: General<br>requirements for product safety |
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K210484
| Standard<br>Number | Standard<br>Organization | Recognition<br>Number | Standard Title |
|---------------------------|--------------------------|-----------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------|
| 62304:2006/<br>AMD 1:2015 | IEC | 13-79 | Medical device software - Software life cycle<br>processes |
| EC57: 2012 | ANSI/AAMI | 3-118 | Testing and Reporting Performance Results of<br>Cardiac Rhythm and ST Segment<br>Measurement Algorithms |
| 60601-1:2005<br>A1:2012 | AAMI/ANSI | 19-4 | Medical electrical equipment - Part 1: General<br>requirements for basic safety and essential<br>performance (Clause 14)<br>(IEC 60601-1:2005, mod) |
#### Predetermined Change Control Plan
The Zelda AI ECG Classification System is powered by deep-learning neural architectures for AF and Pause detection based on the residual convolutional network and ensemble models. Medtronic will make future algorithm improvements under a Predetermined Change Control Plan (PCCP). In the plan, a protocol was provided to specify the methods to achieve and appropriately control the risks of the anticipated types of modifications described in the Software as a Medical Device (SaMD) Pre-specifications (SPS). The planned changes include 1) changing the threshold, 2) re-training the algorithm on data labeled following the original protocol and data labeled following an alternate protocol. 3) pre-training the algorithm. Assessment metrics, acceptance criteria, and statistical methods have been described for the performance testing of the proposed changes. Information on the deployment and post market surveillance of the algorithm are also provided for the proposed changes.
#### Conclusion
The results of the testing met the design requirements and specified acceptance criteria and did not raise new safety or performance issues. Therefore, the LINQ II ICM Model LNQ22 when used with the Zelda AI ECG Classification System Models ZA400, ZA410, ZA420 described in this submission results in a device that is substantially equivalent to the predicate LINQ II ICM Model LNQ22 (K200795).
The proposed modifications in the predetermined change control plan (PCCP) of the 510(k) submission outlined anticipated modifications to the Zelda AI ECG Classification System Models ZA400, ZA410, ZA420, and the methods that will be utilized to implement those modifications in a controlled and deliberate manner while maintaining safety and efficacy. In accordance with the PCCP, market release of any modifications will only occur after the modified algorithms are proven to achieve superior performance, increasing sensitivity and/or specificity, while maintaining or improving other performance metrics. The PCCP does not include provisions for implementation of adaptive algorithms that will continuously learn in the field and all algorithm modifications will be locked prior to release to the field.
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Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.